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<title>Public Responses to Engineering Equality: Gender Quotas and Satisfaction with Democracy: Supplementary Replication Materials</title>

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) 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t=V(this),e=t.uniqueId().attr("id"),i=t.next(),s=i.uniqueId().attr("id");t.attr("aria-controls",s),i.attr("aria-labelledby",e)}).next().attr("role","tabpanel"),this.headers.not(this.active).attr({"aria-selected":"false","aria-expanded":"false",tabIndex:-1}).next().attr({"aria-hidden":"true"}).hide(),this.active.length?this.active.attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0}).next().attr({"aria-hidden":"false"}):this.headers.eq(0).attr("tabIndex",0),this._createIcons(),this._setupEvents(t.event),"fill"===e?(i=s.height(),this.element.siblings(":visible").each(function(){var t=V(this),e=t.css("position");"absolute"!==e&&"fixed"!==e&&(i-=t.outerHeight(!0))}),this.headers.each(function(){i-=V(this).outerHeight(!0)}),this.headers.next().each(function(){V(this).height(Math.max(0,i-V(this).innerHeight()+V(this).height()))}).css("overflow","auto")):"auto"===e&&(i=0,this.headers.next().each(function(){var t=V(this).is(":visible");t||V(this).show(),i=Math.max(i,V(this).css("height","").height()),t||V(this).hide()}).height(i))},_activate:function(t){t=this._findActive(t)[0];t!==this.active[0]&&(t=t||this.active[0],this._eventHandler({target:t,currentTarget:t,preventDefault:V.noop}))},_findActive:function(t){return"number"==typeof t?this.headers.eq(t):V()},_setupEvents:function(t){var i={keydown:"_keydown"};t&&V.each(t.split(" "),function(t,e){i[e]="_eventHandler"}),this._off(this.headers.add(this.headers.next())),this._on(this.headers,i),this._on(this.headers.next(),{keydown:"_panelKeyDown"}),this._hoverable(this.headers),this._focusable(this.headers)},_eventHandler:function(t){var 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e=t.newPanel,i=this.prevShow.length?this.prevShow:t.oldPanel;this.prevShow.add(this.prevHide).stop(!0,!0),this.prevShow=e,this.prevHide=i,this.options.animate?this._animate(e,i,t):(i.hide(),e.show(),this._toggleComplete(t)),i.attr({"aria-hidden":"true"}),i.prev().attr({"aria-selected":"false","aria-expanded":"false"}),e.length&&i.length?i.prev().attr({tabIndex:-1,"aria-expanded":"false"}):e.length&&this.headers.filter(function(){return 0===parseInt(V(this).attr("tabIndex"),10)}).attr("tabIndex",-1),e.attr("aria-hidden","false").prev().attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0})},_animate:function(t,i,e){var s,n,o,a=this,r=0,l=t.css("box-sizing"),h=t.length&&(!i.length||t.index()<i.index()),c=this.options.animate||{},u=h&&c.down||c,h=function(){a._toggleComplete(e)};return n=(n="string"==typeof u?u:n)||u.easing||c.easing,o=(o="number"==typeof u?u:o)||u.duration||c.duration,i.length?t.length?(s=t.show().outerHeight(),i.animate(this.hideProps,{duration:o,easing:n,step:function(t,e){e.now=Math.round(t)}}),void t.hide().animate(this.showProps,{duration:o,easing:n,complete:h,step:function(t,e){e.now=Math.round(t),"height"!==e.prop?"content-box"===l&&(r+=e.now):"content"!==a.options.heightStyle&&(e.now=Math.round(s-i.outerHeight()-r),r=0)}})):i.animate(this.hideProps,o,n,h):t.animate(this.showProps,o,n,h)},_toggleComplete:function(t){var e=t.oldPanel,i=e.prev();this._removeClass(e,"ui-accordion-content-active"),this._removeClass(i,"ui-accordion-header-active")._addClass(i,"ui-accordion-header-collapsed"),e.length&&(e.parent()[0].className=e.parent()[0].className),this._trigger("activate",null,t)}}),V.ui.safeActiveElement=function(e){var i;try{i=e.activeElement}catch(t){i=e.body}return i=!(i=i||e.body).nodeName?e.body:i},V.widget("ui.menu",{version:"1.13.2",defaultElement:"<ul>",delay:300,options:{icons:{submenu:"ui-icon-caret-1-e"},items:"> *",menus:"ul",position:{my:"left top",at:"right top"},role:"menu",blur:null,focus:null,select:null},_create:function(){this.activeMenu=this.element,this.mouseHandled=!1,this.lastMousePosition={x:null,y:null},this.element.uniqueId().attr({role:this.options.role,tabIndex:0}),this._addClass("ui-menu","ui-widget ui-widget-content"),this._on({"mousedown .ui-menu-item":function(t){t.preventDefault(),this._activateItem(t)},"click .ui-menu-item":function(t){var e=V(t.target),i=V(V.ui.safeActiveElement(this.document[0]));!this.mouseHandled&&e.not(".ui-state-disabled").length&&(this.select(t),t.isPropagationStopped()||(this.mouseHandled=!0),e.has(".ui-menu").length?this.expand(t):!this.element.is(":focus")&&i.closest(".ui-menu").length&&(this.element.trigger("focus",[!0]),this.active&&1===this.active.parents(".ui-menu").length&&clearTimeout(this.timer)))},"mouseenter .ui-menu-item":"_activateItem","mousemove .ui-menu-item":"_activateItem",mouseleave:"collapseAll","mouseleave .ui-menu":"collapseAll",focus:function(t,e){var i=this.active||this._menuItems().first();e||this.focus(t,i)},blur:function(t){this._delay(function(){V.contains(this.element[0],V.ui.safeActiveElement(this.document[0]))||this.collapseAll(t)})},keydown:"_keydown"}),this.refresh(),this._on(this.document,{click:function(t){this._closeOnDocumentClick(t)&&this.collapseAll(t,!0),this.mouseHandled=!1}})},_activateItem:function(t){var e,i;this.previousFilter||t.clientX===this.lastMousePosition.x&&t.clientY===this.lastMousePosition.y||(this.lastMousePosition={x:t.clientX,y:t.clientY},e=V(t.target).closest(".ui-menu-item"),i=V(t.currentTarget),e[0]===i[0]&&(i.is(".ui-state-active")||(this._removeClass(i.siblings().children(".ui-state-active"),null,"ui-state-active"),this.focus(t,i))))},_destroy:function(){var t=this.element.find(".ui-menu-item").removeAttr("role aria-disabled").children(".ui-menu-item-wrapper").removeUniqueId().removeAttr("tabIndex role aria-haspopup");this.element.removeAttr("aria-activedescendant").find(".ui-menu").addBack().removeAttr("role aria-labelledby aria-expanded aria-hidden aria-disabled tabIndex").removeUniqueId().show(),t.children().each(function(){var t=V(this);t.data("ui-menu-submenu-caret")&&t.remove()})},_keydown:function(t){var e,i,s,n=!0;switch(t.keyCode){case V.ui.keyCode.PAGE_UP:this.previousPage(t);break;case V.ui.keyCode.PAGE_DOWN:this.nextPage(t);break;case V.ui.keyCode.HOME:this._move("first","first",t);break;case V.ui.keyCode.END:this._move("last","last",t);break;case V.ui.keyCode.UP:this.previous(t);break;case V.ui.keyCode.DOWN:this.next(t);break;case V.ui.keyCode.LEFT:this.collapse(t);break;case V.ui.keyCode.RIGHT:this.active&&!this.active.is(".ui-state-disabled")&&this.expand(t);break;case V.ui.keyCode.ENTER:case V.ui.keyCode.SPACE:this._activate(t);break;case V.ui.keyCode.ESCAPE:this.collapse(t);break;default:e=this.previousFilter||"",s=n=!1,i=96<=t.keyCode&&t.keyCode<=105?(t.keyCode-96).toString():String.fromCharCode(t.keyCode),clearTimeout(this.filterTimer),i===e?s=!0:i=e+i,e=this._filterMenuItems(i),(e=s&&-1!==e.index(this.active.next())?this.active.nextAll(".ui-menu-item"):e).length||(i=String.fromCharCode(t.keyCode),e=this._filterMenuItems(i)),e.length?(this.focus(t,e),this.previousFilter=i,this.filterTimer=this._delay(function(){delete this.previousFilter},1e3)):delete this.previousFilter}n&&t.preventDefault()},_activate:function(t){this.active&&!this.active.is(".ui-state-disabled")&&(this.active.children("[aria-haspopup='true']").length?this.expand(t):this.select(t))},refresh:function(){var t,e,s=this,n=this.options.icons.submenu,i=this.element.find(this.options.menus);this._toggleClass("ui-menu-icons",null,!!this.element.find(".ui-icon").length),e=i.filter(":not(.ui-menu)").hide().attr({role:this.options.role,"aria-hidden":"true","aria-expanded":"false"}).each(function(){var t=V(this),e=t.prev(),i=V("<span>").data("ui-menu-submenu-caret",!0);s._addClass(i,"ui-menu-icon","ui-icon "+n),e.attr("aria-haspopup","true").prepend(i),t.attr("aria-labelledby",e.attr("id"))}),this._addClass(e,"ui-menu","ui-widget ui-widget-content ui-front"),(t=i.add(this.element).find(this.options.items)).not(".ui-menu-item").each(function(){var t=V(this);s._isDivider(t)&&s._addClass(t,"ui-menu-divider","ui-widget-content")}),i=(e=t.not(".ui-menu-item, .ui-menu-divider")).children().not(".ui-menu").uniqueId().attr({tabIndex:-1,role:this._itemRole()}),this._addClass(e,"ui-menu-item")._addClass(i,"ui-menu-item-wrapper"),t.filter(".ui-state-disabled").attr("aria-disabled","true"),this.active&&!V.contains(this.element[0],this.active[0])&&this.blur()},_itemRole:function(){return{menu:"menuitem",listbox:"option"}[this.options.role]},_setOption:function(t,e){var i;"icons"===t&&(i=this.element.find(".ui-menu-icon"),this._removeClass(i,null,this.options.icons.submenu)._addClass(i,null,e.submenu)),this._super(t,e)},_setOptionDisabled:function(t){this._super(t),this.element.attr("aria-disabled",String(t)),this._toggleClass(null,"ui-state-disabled",!!t)},focus:function(t,e){var i;this.blur(t,t&&"focus"===t.type),this._scrollIntoView(e),this.active=e.first(),i=this.active.children(".ui-menu-item-wrapper"),this._addClass(i,null,"ui-state-active"),this.options.role&&this.element.attr("aria-activedescendant",i.attr("id")),i=this.active.parent().closest(".ui-menu-item").children(".ui-menu-item-wrapper"),this._addClass(i,null,"ui-state-active"),t&&"keydown"===t.type?this._close():this.timer=this._delay(function(){this._close()},this.delay),(i=e.children(".ui-menu")).length&&t&&/^mouse/.test(t.type)&&this._startOpening(i),this.activeMenu=e.parent(),this._trigger("focus",t,{item:e})},_scrollIntoView:function(t){var e,i,s;this._hasScroll()&&(i=parseFloat(V.css(this.activeMenu[0],"borderTopWidth"))||0,s=parseFloat(V.css(this.activeMenu[0],"paddingTop"))||0,e=t.offset().top-this.activeMenu.offset().top-i-s,i=this.activeMenu.scrollTop(),s=this.activeMenu.height(),t=t.outerHeight(),e<0?this.activeMenu.scrollTop(i+e):s<e+t&&this.activeMenu.scrollTop(i+e-s+t))},blur:function(t,e){e||clearTimeout(this.timer),this.active&&(this._removeClass(this.active.children(".ui-menu-item-wrapper"),null,"ui-state-active"),this._trigger("blur",t,{item:this.active}),this.active=null)},_startOpening:function(t){clearTimeout(this.timer),"true"===t.attr("aria-hidden")&&(this.timer=this._delay(function(){this._close(),this._open(t)},this.delay))},_open:function(t){var e=V.extend({of:this.active},this.options.position);clearTimeout(this.timer),this.element.find(".ui-menu").not(t.parents(".ui-menu")).hide().attr("aria-hidden","true"),t.show().removeAttr("aria-hidden").attr("aria-expanded","true").position(e)},collapseAll:function(e,i){clearTimeout(this.timer),this.timer=this._delay(function(){var t=i?this.element:V(e&&e.target).closest(this.element.find(".ui-menu"));t.length||(t=this.element),this._close(t),this.blur(e),this._removeClass(t.find(".ui-state-active"),null,"ui-state-active"),this.activeMenu=t},i?0:this.delay)},_close:function(t){(t=t||(this.active?this.active.parent():this.element)).find(".ui-menu").hide().attr("aria-hidden","true").attr("aria-expanded","false")},_closeOnDocumentClick:function(t){return!V(t.target).closest(".ui-menu").length},_isDivider:function(t){return!/[^\-\u2014\u2013\s]/.test(t.text())},collapse:function(t){var e=this.active&&this.active.parent().closest(".ui-menu-item",this.element);e&&e.length&&(this._close(),this.focus(t,e))},expand:function(t){var e=this.active&&this._menuItems(this.active.children(".ui-menu")).first();e&&e.length&&(this._open(e.parent()),this._delay(function(){this.focus(t,e)}))},next:function(t){this._move("next","first",t)},previous:function(t){this._move("prev","last",t)},isFirstItem:function(){return this.active&&!this.active.prevAll(".ui-menu-item").length},isLastItem:function(){return this.active&&!this.active.nextAll(".ui-menu-item").length},_menuItems:function(t){return(t||this.element).find(this.options.items).filter(".ui-menu-item")},_move:function(t,e,i){var s;(s=this.active?"first"===t||"last"===t?this.active["first"===t?"prevAll":"nextAll"](".ui-menu-item").last():this.active[t+"All"](".ui-menu-item").first():s)&&s.length&&this.active||(s=this._menuItems(this.activeMenu)[e]()),this.focus(i,s)},nextPage:function(t){var e,i,s;this.active?this.isLastItem()||(this._hasScroll()?(i=this.active.offset().top,s=this.element.innerHeight(),0===V.fn.jquery.indexOf("3.2.")&&(s+=this.element[0].offsetHeight-this.element.outerHeight()),this.active.nextAll(".ui-menu-item").each(function(){return(e=V(this)).offset().top-i-s<0}),this.focus(t,e)):this.focus(t,this._menuItems(this.activeMenu)[this.active?"last":"first"]())):this.next(t)},previousPage:function(t){var e,i,s;this.active?this.isFirstItem()||(this._hasScroll()?(i=this.active.offset().top,s=this.element.innerHeight(),0===V.fn.jquery.indexOf("3.2.")&&(s+=this.element[0].offsetHeight-this.element.outerHeight()),this.active.prevAll(".ui-menu-item").each(function(){return 0<(e=V(this)).offset().top-i+s}),this.focus(t,e)):this.focus(t,this._menuItems(this.activeMenu).first())):this.next(t)},_hasScroll:function(){return this.element.outerHeight()<this.element.prop("scrollHeight")},select:function(t){this.active=this.active||V(t.target).closest(".ui-menu-item");var e={item:this.active};this.active.has(".ui-menu").length||this.collapseAll(t,!0),this._trigger("select",t,e)},_filterMenuItems:function(t){var t=t.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&"),e=new RegExp("^"+t,"i");return this.activeMenu.find(this.options.items).filter(".ui-menu-item").filter(function(){return e.test(String.prototype.trim.call(V(this).children(".ui-menu-item-wrapper").text()))})}});V.widget("ui.autocomplete",{version:"1.13.2",defaultElement:"<input>",options:{appendTo:null,autoFocus:!1,delay:300,minLength:1,position:{my:"left top",at:"left bottom",collision:"none"},source:null,change:null,close:null,focus:null,open:null,response:null,search:null,select:null},requestIndex:0,pending:0,liveRegionTimer:null,_create:function(){var i,s,n,t=this.element[0].nodeName.toLowerCase(),e="textarea"===t,t="input"===t;this.isMultiLine=e||!t&&this._isContentEditable(this.element),this.valueMethod=this.element[e||t?"val":"text"],this.isNewMenu=!0,this._addClass("ui-autocomplete-input"),this.element.attr("autocomplete","off"),this._on(this.element,{keydown:function(t){if(this.element.prop("readOnly"))s=n=i=!0;else{s=n=i=!1;var e=V.ui.keyCode;switch(t.keyCode){case e.PAGE_UP:i=!0,this._move("previousPage",t);break;case e.PAGE_DOWN:i=!0,this._move("nextPage",t);break;case e.UP:i=!0,this._keyEvent("previous",t);break;case e.DOWN:i=!0,this._keyEvent("next",t);break;case e.ENTER:this.menu.active&&(i=!0,t.preventDefault(),this.menu.select(t));break;case e.TAB:this.menu.active&&this.menu.select(t);break;case e.ESCAPE:this.menu.element.is(":visible")&&(this.isMultiLine||this._value(this.term),this.close(t),t.preventDefault());break;default:s=!0,this._searchTimeout(t)}}},keypress:function(t){if(i)return i=!1,void(this.isMultiLine&&!this.menu.element.is(":visible")||t.preventDefault());if(!s){var e=V.ui.keyCode;switch(t.keyCode){case e.PAGE_UP:this._move("previousPage",t);break;case e.PAGE_DOWN:this._move("nextPage",t);break;case e.UP:this._keyEvent("previous",t);break;case e.DOWN:this._keyEvent("next",t)}}},input:function(t){if(n)return n=!1,void t.preventDefault();this._searchTimeout(t)},focus:function(){this.selectedItem=null,this.previous=this._value()},blur:function(t){clearTimeout(this.searching),this.close(t),this._change(t)}}),this._initSource(),this.menu=V("<ul>").appendTo(this._appendTo()).menu({role:null}).hide().attr({unselectable:"on"}).menu("instance"),this._addClass(this.menu.element,"ui-autocomplete","ui-front"),this._on(this.menu.element,{mousedown:function(t){t.preventDefault()},menufocus:function(t,e){var i,s;if(this.isNewMenu&&(this.isNewMenu=!1,t.originalEvent&&/^mouse/.test(t.originalEvent.type)))return this.menu.blur(),void this.document.one("mousemove",function(){V(t.target).trigger(t.originalEvent)});s=e.item.data("ui-autocomplete-item"),!1!==this._trigger("focus",t,{item:s})&&t.originalEvent&&/^key/.test(t.originalEvent.type)&&this._value(s.value),(i=e.item.attr("aria-label")||s.value)&&String.prototype.trim.call(i).length&&(clearTimeout(this.liveRegionTimer),this.liveRegionTimer=this._delay(function(){this.liveRegion.html(V("<div>").text(i))},100))},menuselect:function(t,e){var i=e.item.data("ui-autocomplete-item"),s=this.previous;this.element[0]!==V.ui.safeActiveElement(this.document[0])&&(this.element.trigger("focus"),this.previous=s,this._delay(function(){this.previous=s,this.selectedItem=i})),!1!==this._trigger("select",t,{item:i})&&this._value(i.value),this.term=this._value(),this.close(t),this.selectedItem=i}}),this.liveRegion=V("<div>",{role:"status","aria-live":"assertive","aria-relevant":"additions"}).appendTo(this.document[0].body),this._addClass(this.liveRegion,null,"ui-helper-hidden-accessible"),this._on(this.window,{beforeunload:function(){this.element.removeAttr("autocomplete")}})},_destroy:function(){clearTimeout(this.searching),this.element.removeAttr("autocomplete"),this.menu.element.remove(),this.liveRegion.remove()},_setOption:function(t,e){this._super(t,e),"source"===t&&this._initSource(),"appendTo"===t&&this.menu.element.appendTo(this._appendTo()),"disabled"===t&&e&&this.xhr&&this.xhr.abort()},_isEventTargetInWidget:function(t){var e=this.menu.element[0];return t.target===this.element[0]||t.target===e||V.contains(e,t.target)},_closeOnClickOutside:function(t){this._isEventTargetInWidget(t)||this.close()},_appendTo:function(){var t=this.options.appendTo;return t=!(t=!(t=t&&(t.jquery||t.nodeType?V(t):this.document.find(t).eq(0)))||!t[0]?this.element.closest(".ui-front, dialog"):t).length?this.document[0].body:t},_initSource:function(){var i,s,n=this;Array.isArray(this.options.source)?(i=this.options.source,this.source=function(t,e){e(V.ui.autocomplete.filter(i,t.term))}):"string"==typeof this.options.source?(s=this.options.source,this.source=function(t,e){n.xhr&&n.xhr.abort(),n.xhr=V.ajax({url:s,data:t,dataType:"json",success:function(t){e(t)},error:function(){e([])}})}):this.source=this.options.source},_searchTimeout:function(s){clearTimeout(this.searching),this.searching=this._delay(function(){var t=this.term===this._value(),e=this.menu.element.is(":visible"),i=s.altKey||s.ctrlKey||s.metaKey||s.shiftKey;t&&(e||i)||(this.selectedItem=null,this.search(null,s))},this.options.delay)},search:function(t,e){return t=null!=t?t:this._value(),this.term=this._value(),t.length<this.options.minLength?this.close(e):!1!==this._trigger("search",e)?this._search(t):void 0},_search:function(t){this.pending++,this._addClass("ui-autocomplete-loading"),this.cancelSearch=!1,this.source({term:t},this._response())},_response:function(){var e=++this.requestIndex;return function(t){e===this.requestIndex&&this.__response(t),this.pending--,this.pending||this._removeClass("ui-autocomplete-loading")}.bind(this)},__response:function(t){t=t&&this._normalize(t),this._trigger("response",null,{content:t}),!this.options.disabled&&t&&t.length&&!this.cancelSearch?(this._suggest(t),this._trigger("open")):this._close()},close:function(t){this.cancelSearch=!0,this._close(t)},_close:function(t){this._off(this.document,"mousedown"),this.menu.element.is(":visible")&&(this.menu.element.hide(),this.menu.blur(),this.isNewMenu=!0,this._trigger("close",t))},_change:function(t){this.previous!==this._value()&&this._trigger("change",t,{item:this.selectedItem})},_normalize:function(t){return t.length&&t[0].label&&t[0].value?t:V.map(t,function(t){return"string"==typeof t?{label:t,value:t}:V.extend({},t,{label:t.label||t.value,value:t.value||t.label})})},_suggest:function(t){var e=this.menu.element.empty();this._renderMenu(e,t),this.isNewMenu=!0,this.menu.refresh(),e.show(),this._resizeMenu(),e.position(V.extend({of:this.element},this.options.position)),this.options.autoFocus&&this.menu.next(),this._on(this.document,{mousedown:"_closeOnClickOutside"})},_resizeMenu:function(){var t=this.menu.element;t.outerWidth(Math.max(t.width("").outerWidth()+1,this.element.outerWidth()))},_renderMenu:function(i,t){var s=this;V.each(t,function(t,e){s._renderItemData(i,e)})},_renderItemData:function(t,e){return this._renderItem(t,e).data("ui-autocomplete-item",e)},_renderItem:function(t,e){return V("<li>").append(V("<div>").text(e.label)).appendTo(t)},_move:function(t,e){if(this.menu.element.is(":visible"))return this.menu.isFirstItem()&&/^previous/.test(t)||this.menu.isLastItem()&&/^next/.test(t)?(this.isMultiLine||this._value(this.term),void this.menu.blur()):void this.menu[t](e);this.search(null,e)},widget:function(){return this.menu.element},_value:function(){return this.valueMethod.apply(this.element,arguments)},_keyEvent:function(t,e){this.isMultiLine&&!this.menu.element.is(":visible")||(this._move(t,e),e.preventDefault())},_isContentEditable:function(t){if(!t.length)return!1;var e=t.prop("contentEditable");return"inherit"===e?this._isContentEditable(t.parent()):"true"===e}}),V.extend(V.ui.autocomplete,{escapeRegex:function(t){return t.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&")},filter:function(t,e){var i=new RegExp(V.ui.autocomplete.escapeRegex(e),"i");return V.grep(t,function(t){return i.test(t.label||t.value||t)})}}),V.widget("ui.autocomplete",V.ui.autocomplete,{options:{messages:{noResults:"No search results.",results:function(t){return t+(1<t?" results are":" result is")+" available, use up and down arrow keys to navigate."}}},__response:function(t){var e;this._superApply(arguments),this.options.disabled||this.cancelSearch||(e=t&&t.length?this.options.messages.results(t.length):this.options.messages.noResults,clearTimeout(this.liveRegionTimer),this.liveRegionTimer=this._delay(function(){this.liveRegion.html(V("<div>").text(e))},100))}});V.ui.autocomplete;var tt=/ui-corner-([a-z]){2,6}/g;V.widget("ui.controlgroup",{version:"1.13.2",defaultElement:"<div>",options:{direction:"horizontal",disabled:null,onlyVisible:!0,items:{button:"input[type=button], input[type=submit], input[type=reset], button, a",controlgroupLabel:".ui-controlgroup-label",checkboxradio:"input[type='checkbox'], input[type='radio']",selectmenu:"select",spinner:".ui-spinner-input"}},_create:function(){this._enhance()},_enhance:function(){this.element.attr("role","toolbar"),this.refresh()},_destroy:function(){this._callChildMethod("destroy"),this.childWidgets.removeData("ui-controlgroup-data"),this.element.removeAttr("role"),this.options.items.controlgroupLabel&&this.element.find(this.options.items.controlgroupLabel).find(".ui-controlgroup-label-contents").contents().unwrap()},_initWidgets:function(){var o=this,a=[];V.each(this.options.items,function(s,t){var e,n={};if(t)return"controlgroupLabel"===s?((e=o.element.find(t)).each(function(){var t=V(this);t.children(".ui-controlgroup-label-contents").length||t.contents().wrapAll("<span class='ui-controlgroup-label-contents'></span>")}),o._addClass(e,null,"ui-widget ui-widget-content ui-state-default"),void(a=a.concat(e.get()))):void(V.fn[s]&&(n=o["_"+s+"Options"]?o["_"+s+"Options"]("middle"):{classes:{}},o.element.find(t).each(function(){var t=V(this),e=t[s]("instance"),i=V.widget.extend({},n);"button"===s&&t.parent(".ui-spinner").length||((e=e||t[s]()[s]("instance"))&&(i.classes=o._resolveClassesValues(i.classes,e)),t[s](i),i=t[s]("widget"),V.data(i[0],"ui-controlgroup-data",e||t[s]("instance")),a.push(i[0]))})))}),this.childWidgets=V(V.uniqueSort(a)),this._addClass(this.childWidgets,"ui-controlgroup-item")},_callChildMethod:function(e){this.childWidgets.each(function(){var t=V(this).data("ui-controlgroup-data");t&&t[e]&&t[e]()})},_updateCornerClass:function(t,e){e=this._buildSimpleOptions(e,"label").classes.label;this._removeClass(t,null,"ui-corner-top ui-corner-bottom ui-corner-left ui-corner-right ui-corner-all"),this._addClass(t,null,e)},_buildSimpleOptions:function(t,e){var i="vertical"===this.options.direction,s={classes:{}};return s.classes[e]={middle:"",first:"ui-corner-"+(i?"top":"left"),last:"ui-corner-"+(i?"bottom":"right"),only:"ui-corner-all"}[t],s},_spinnerOptions:function(t){t=this._buildSimpleOptions(t,"ui-spinner");return t.classes["ui-spinner-up"]="",t.classes["ui-spinner-down"]="",t},_buttonOptions:function(t){return this._buildSimpleOptions(t,"ui-button")},_checkboxradioOptions:function(t){return this._buildSimpleOptions(t,"ui-checkboxradio-label")},_selectmenuOptions:function(t){var e="vertical"===this.options.direction;return{width:e&&"auto",classes:{middle:{"ui-selectmenu-button-open":"","ui-selectmenu-button-closed":""},first:{"ui-selectmenu-button-open":"ui-corner-"+(e?"top":"tl"),"ui-selectmenu-button-closed":"ui-corner-"+(e?"top":"left")},last:{"ui-selectmenu-button-open":e?"":"ui-corner-tr","ui-selectmenu-button-closed":"ui-corner-"+(e?"bottom":"right")},only:{"ui-selectmenu-button-open":"ui-corner-top","ui-selectmenu-button-closed":"ui-corner-all"}}[t]}},_resolveClassesValues:function(i,s){var n={};return V.each(i,function(t){var e=s.options.classes[t]||"",e=String.prototype.trim.call(e.replace(tt,""));n[t]=(e+" "+i[t]).replace(/\s+/g," ")}),n},_setOption:function(t,e){"direction"===t&&this._removeClass("ui-controlgroup-"+this.options.direction),this._super(t,e),"disabled"!==t?this.refresh():this._callChildMethod(e?"disable":"enable")},refresh:function(){var n,o=this;this._addClass("ui-controlgroup ui-controlgroup-"+this.options.direction),"horizontal"===this.options.direction&&this._addClass(null,"ui-helper-clearfix"),this._initWidgets(),n=this.childWidgets,(n=this.options.onlyVisible?n.filter(":visible"):n).length&&(V.each(["first","last"],function(t,e){var i,s=n[e]().data("ui-controlgroup-data");s&&o["_"+s.widgetName+"Options"]?((i=o["_"+s.widgetName+"Options"](1===n.length?"only":e)).classes=o._resolveClassesValues(i.classes,s),s.element[s.widgetName](i)):o._updateCornerClass(n[e](),e)}),this._callChildMethod("refresh"))}});V.widget("ui.checkboxradio",[V.ui.formResetMixin,{version:"1.13.2",options:{disabled:null,label:null,icon:!0,classes:{"ui-checkboxradio-label":"ui-corner-all","ui-checkboxradio-icon":"ui-corner-all"}},_getCreateOptions:function(){var t,e=this._super()||{};return this._readType(),t=this.element.labels(),this.label=V(t[t.length-1]),this.label.length||V.error("No label found for checkboxradio widget"),this.originalLabel="",(t=this.label.contents().not(this.element[0])).length&&(this.originalLabel+=t.clone().wrapAll("<div></div>").parent().html()),this.originalLabel&&(e.label=this.originalLabel),null!=(t=this.element[0].disabled)&&(e.disabled=t),e},_create:function(){var t=this.element[0].checked;this._bindFormResetHandler(),null==this.options.disabled&&(this.options.disabled=this.element[0].disabled),this._setOption("disabled",this.options.disabled),this._addClass("ui-checkboxradio","ui-helper-hidden-accessible"),this._addClass(this.label,"ui-checkboxradio-label","ui-button ui-widget"),"radio"===this.type&&this._addClass(this.label,"ui-checkboxradio-radio-label"),this.options.label&&this.options.label!==this.originalLabel?this._updateLabel():this.originalLabel&&(this.options.label=this.originalLabel),this._enhance(),t&&this._addClass(this.label,"ui-checkboxradio-checked","ui-state-active"),this._on({change:"_toggleClasses",focus:function(){this._addClass(this.label,null,"ui-state-focus ui-visual-focus")},blur:function(){this._removeClass(this.label,null,"ui-state-focus ui-visual-focus")}})},_readType:function(){var t=this.element[0].nodeName.toLowerCase();this.type=this.element[0].type,"input"===t&&/radio|checkbox/.test(this.type)||V.error("Can't create checkboxradio on element.nodeName="+t+" and element.type="+this.type)},_enhance:function(){this._updateIcon(this.element[0].checked)},widget:function(){return this.label},_getRadioGroup:function(){var t=this.element[0].name,e="input[name='"+V.escapeSelector(t)+"']";return t?(this.form.length?V(this.form[0].elements).filter(e):V(e).filter(function(){return 0===V(this)._form().length})).not(this.element):V([])},_toggleClasses:function(){var t=this.element[0].checked;this._toggleClass(this.label,"ui-checkboxradio-checked","ui-state-active",t),this.options.icon&&"checkbox"===this.type&&this._toggleClass(this.icon,null,"ui-icon-check ui-state-checked",t)._toggleClass(this.icon,null,"ui-icon-blank",!t),"radio"===this.type&&this._getRadioGroup().each(function(){var t=V(this).checkboxradio("instance");t&&t._removeClass(t.label,"ui-checkboxradio-checked","ui-state-active")})},_destroy:function(){this._unbindFormResetHandler(),this.icon&&(this.icon.remove(),this.iconSpace.remove())},_setOption:function(t,e){if("label"!==t||e){if(this._super(t,e),"disabled"===t)return this._toggleClass(this.label,null,"ui-state-disabled",e),void(this.element[0].disabled=e);this.refresh()}},_updateIcon:function(t){var e="ui-icon ui-icon-background ";this.options.icon?(this.icon||(this.icon=V("<span>"),this.iconSpace=V("<span> </span>"),this._addClass(this.iconSpace,"ui-checkboxradio-icon-space")),"checkbox"===this.type?(e+=t?"ui-icon-check ui-state-checked":"ui-icon-blank",this._removeClass(this.icon,null,t?"ui-icon-blank":"ui-icon-check")):e+="ui-icon-blank",this._addClass(this.icon,"ui-checkboxradio-icon",e),t||this._removeClass(this.icon,null,"ui-icon-check ui-state-checked"),this.icon.prependTo(this.label).after(this.iconSpace)):void 0!==this.icon&&(this.icon.remove(),this.iconSpace.remove(),delete this.icon)},_updateLabel:function(){var t=this.label.contents().not(this.element[0]);this.icon&&(t=t.not(this.icon[0])),(t=this.iconSpace?t.not(this.iconSpace[0]):t).remove(),this.label.append(this.options.label)},refresh:function(){var t=this.element[0].checked,e=this.element[0].disabled;this._updateIcon(t),this._toggleClass(this.label,"ui-checkboxradio-checked","ui-state-active",t),null!==this.options.label&&this._updateLabel(),e!==this.options.disabled&&this._setOptions({disabled:e})}}]);var et;V.ui.checkboxradio;V.widget("ui.button",{version:"1.13.2",defaultElement:"<button>",options:{classes:{"ui-button":"ui-corner-all"},disabled:null,icon:null,iconPosition:"beginning",label:null,showLabel:!0},_getCreateOptions:function(){var t,e=this._super()||{};return this.isInput=this.element.is("input"),null!=(t=this.element[0].disabled)&&(e.disabled=t),this.originalLabel=this.isInput?this.element.val():this.element.html(),this.originalLabel&&(e.label=this.originalLabel),e},_create:function(){!this.option.showLabel&!this.options.icon&&(this.options.showLabel=!0),null==this.options.disabled&&(this.options.disabled=this.element[0].disabled||!1),this.hasTitle=!!this.element.attr("title"),this.options.label&&this.options.label!==this.originalLabel&&(this.isInput?this.element.val(this.options.label):this.element.html(this.options.label)),this._addClass("ui-button","ui-widget"),this._setOption("disabled",this.options.disabled),this._enhance(),this.element.is("a")&&this._on({keyup:function(t){t.keyCode===V.ui.keyCode.SPACE&&(t.preventDefault(),this.element[0].click?this.element[0].click():this.element.trigger("click"))}})},_enhance:function(){this.element.is("button")||this.element.attr("role","button"),this.options.icon&&(this._updateIcon("icon",this.options.icon),this._updateTooltip())},_updateTooltip:function(){this.title=this.element.attr("title"),this.options.showLabel||this.title||this.element.attr("title",this.options.label)},_updateIcon:function(t,e){var i="iconPosition"!==t,s=i?this.options.iconPosition:e,t="top"===s||"bottom"===s;this.icon?i&&this._removeClass(this.icon,null,this.options.icon):(this.icon=V("<span>"),this._addClass(this.icon,"ui-button-icon","ui-icon"),this.options.showLabel||this._addClass("ui-button-icon-only")),i&&this._addClass(this.icon,null,e),this._attachIcon(s),t?(this._addClass(this.icon,null,"ui-widget-icon-block"),this.iconSpace&&this.iconSpace.remove()):(this.iconSpace||(this.iconSpace=V("<span> </span>"),this._addClass(this.iconSpace,"ui-button-icon-space")),this._removeClass(this.icon,null,"ui-wiget-icon-block"),this._attachIconSpace(s))},_destroy:function(){this.element.removeAttr("role"),this.icon&&this.icon.remove(),this.iconSpace&&this.iconSpace.remove(),this.hasTitle||this.element.removeAttr("title")},_attachIconSpace:function(t){this.icon[/^(?:end|bottom)/.test(t)?"before":"after"](this.iconSpace)},_attachIcon:function(t){this.element[/^(?:end|bottom)/.test(t)?"append":"prepend"](this.icon)},_setOptions:function(t){var e=(void 0===t.showLabel?this.options:t).showLabel,i=(void 0===t.icon?this.options:t).icon;e||i||(t.showLabel=!0),this._super(t)},_setOption:function(t,e){"icon"===t&&(e?this._updateIcon(t,e):this.icon&&(this.icon.remove(),this.iconSpace&&this.iconSpace.remove())),"iconPosition"===t&&this._updateIcon(t,e),"showLabel"===t&&(this._toggleClass("ui-button-icon-only",null,!e),this._updateTooltip()),"label"===t&&(this.isInput?this.element.val(e):(this.element.html(e),this.icon&&(this._attachIcon(this.options.iconPosition),this._attachIconSpace(this.options.iconPosition)))),this._super(t,e),"disabled"===t&&(this._toggleClass(null,"ui-state-disabled",e),(this.element[0].disabled=e)&&this.element.trigger("blur"))},refresh:function(){var t=this.element.is("input, button")?this.element[0].disabled:this.element.hasClass("ui-button-disabled");t!==this.options.disabled&&this._setOptions({disabled:t}),this._updateTooltip()}}),!1!==V.uiBackCompat&&(V.widget("ui.button",V.ui.button,{options:{text:!0,icons:{primary:null,secondary:null}},_create:function(){this.options.showLabel&&!this.options.text&&(this.options.showLabel=this.options.text),!this.options.showLabel&&this.options.text&&(this.options.text=this.options.showLabel),this.options.icon||!this.options.icons.primary&&!this.options.icons.secondary?this.options.icon&&(this.options.icons.primary=this.options.icon):this.options.icons.primary?this.options.icon=this.options.icons.primary:(this.options.icon=this.options.icons.secondary,this.options.iconPosition="end"),this._super()},_setOption:function(t,e){"text"!==t?("showLabel"===t&&(this.options.text=e),"icon"===t&&(this.options.icons.primary=e),"icons"===t&&(e.primary?(this._super("icon",e.primary),this._super("iconPosition","beginning")):e.secondary&&(this._super("icon",e.secondary),this._super("iconPosition","end"))),this._superApply(arguments)):this._super("showLabel",e)}}),V.fn.button=(et=V.fn.button,function(i){var t="string"==typeof i,s=Array.prototype.slice.call(arguments,1),n=this;return t?this.length||"instance"!==i?this.each(function(){var t=V(this).attr("type"),e=V.data(this,"ui-"+("checkbox"!==t&&"radio"!==t?"button":"checkboxradio"));return"instance"===i?(n=e,!1):e?"function"!=typeof e[i]||"_"===i.charAt(0)?V.error("no such method '"+i+"' for button widget instance"):(t=e[i].apply(e,s))!==e&&void 0!==t?(n=t&&t.jquery?n.pushStack(t.get()):t,!1):void 0:V.error("cannot call methods on button prior to initialization; attempted to call method '"+i+"'")}):n=void 0:(s.length&&(i=V.widget.extend.apply(null,[i].concat(s))),this.each(function(){var t=V(this).attr("type"),e="checkbox"!==t&&"radio"!==t?"button":"checkboxradio",t=V.data(this,"ui-"+e);t?(t.option(i||{}),t._init&&t._init()):"button"!=e?V(this).checkboxradio(V.extend({icon:!1},i)):et.call(V(this),i)})),n}),V.fn.buttonset=function(){return V.ui.controlgroup||V.error("Controlgroup widget missing"),"option"===arguments[0]&&"items"===arguments[1]&&arguments[2]?this.controlgroup.apply(this,[arguments[0],"items.button",arguments[2]]):"option"===arguments[0]&&"items"===arguments[1]?this.controlgroup.apply(this,[arguments[0],"items.button"]):("object"==typeof arguments[0]&&arguments[0].items&&(arguments[0].items={button:arguments[0].items}),this.controlgroup.apply(this,arguments))});var it;V.ui.button;function st(){this._curInst=null,this._keyEvent=!1,this._disabledInputs=[],this._datepickerShowing=!1,this._inDialog=!1,this._mainDivId="ui-datepicker-div",this._inlineClass="ui-datepicker-inline",this._appendClass="ui-datepicker-append",this._triggerClass="ui-datepicker-trigger",this._dialogClass="ui-datepicker-dialog",this._disableClass="ui-datepicker-disabled",this._unselectableClass="ui-datepicker-unselectable",this._currentClass="ui-datepicker-current-day",this._dayOverClass="ui-datepicker-days-cell-over",this.regional=[],this.regional[""]={closeText:"Done",prevText:"Prev",nextText:"Next",currentText:"Today",monthNames:["January","February","March","April","May","June","July","August","September","October","November","December"],monthNamesShort:["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],dayNames:["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],dayNamesShort:["Sun","Mon","Tue","Wed","Thu","Fri","Sat"],dayNamesMin:["Su","Mo","Tu","We","Th","Fr","Sa"],weekHeader:"Wk",dateFormat:"mm/dd/yy",firstDay:0,isRTL:!1,showMonthAfterYear:!1,yearSuffix:"",selectMonthLabel:"Select month",selectYearLabel:"Select year"},this._defaults={showOn:"focus",showAnim:"fadeIn",showOptions:{},defaultDate:null,appendText:"",buttonText:"...",buttonImage:"",buttonImageOnly:!1,hideIfNoPrevNext:!1,navigationAsDateFormat:!1,gotoCurrent:!1,changeMonth:!1,changeYear:!1,yearRange:"c-10:c+10",showOtherMonths:!1,selectOtherMonths:!1,showWeek:!1,calculateWeek:this.iso8601Week,shortYearCutoff:"+10",minDate:null,maxDate:null,duration:"fast",beforeShowDay:null,beforeShow:null,onSelect:null,onChangeMonthYear:null,onClose:null,onUpdateDatepicker:null,numberOfMonths:1,showCurrentAtPos:0,stepMonths:1,stepBigMonths:12,altField:"",altFormat:"",constrainInput:!0,showButtonPanel:!1,autoSize:!1,disabled:!1},V.extend(this._defaults,this.regional[""]),this.regional.en=V.extend(!0,{},this.regional[""]),this.regional["en-US"]=V.extend(!0,{},this.regional.en),this.dpDiv=nt(V("<div id='"+this._mainDivId+"' class='ui-datepicker ui-widget ui-widget-content ui-helper-clearfix ui-corner-all'></div>"))}function nt(t){var e="button, .ui-datepicker-prev, .ui-datepicker-next, .ui-datepicker-calendar td a";return t.on("mouseout",e,function(){V(this).removeClass("ui-state-hover"),-1!==this.className.indexOf("ui-datepicker-prev")&&V(this).removeClass("ui-datepicker-prev-hover"),-1!==this.className.indexOf("ui-datepicker-next")&&V(this).removeClass("ui-datepicker-next-hover")}).on("mouseover",e,ot)}function ot(){V.datepicker._isDisabledDatepicker((it.inline?it.dpDiv.parent():it.input)[0])||(V(this).parents(".ui-datepicker-calendar").find("a").removeClass("ui-state-hover"),V(this).addClass("ui-state-hover"),-1!==this.className.indexOf("ui-datepicker-prev")&&V(this).addClass("ui-datepicker-prev-hover"),-1!==this.className.indexOf("ui-datepicker-next")&&V(this).addClass("ui-datepicker-next-hover"))}function at(t,e){for(var i in V.extend(t,e),e)null==e[i]&&(t[i]=e[i]);return t}V.extend(V.ui,{datepicker:{version:"1.13.2"}}),V.extend(st.prototype,{markerClassName:"hasDatepicker",maxRows:4,_widgetDatepicker:function(){return this.dpDiv},setDefaults:function(t){return at(this._defaults,t||{}),this},_attachDatepicker:function(t,e){var i,s=t.nodeName.toLowerCase(),n="div"===s||"span"===s;t.id||(this.uuid+=1,t.id="dp"+this.uuid),(i=this._newInst(V(t),n)).settings=V.extend({},e||{}),"input"===s?this._connectDatepicker(t,i):n&&this._inlineDatepicker(t,i)},_newInst:function(t,e){return{id:t[0].id.replace(/([^A-Za-z0-9_\-])/g,"\\\\$1"),input:t,selectedDay:0,selectedMonth:0,selectedYear:0,drawMonth:0,drawYear:0,inline:e,dpDiv:e?nt(V("<div class='"+this._inlineClass+" ui-datepicker ui-widget ui-widget-content ui-helper-clearfix ui-corner-all'></div>")):this.dpDiv}},_connectDatepicker:function(t,e){var i=V(t);e.append=V([]),e.trigger=V([]),i.hasClass(this.markerClassName)||(this._attachments(i,e),i.addClass(this.markerClassName).on("keydown",this._doKeyDown).on("keypress",this._doKeyPress).on("keyup",this._doKeyUp),this._autoSize(e),V.data(t,"datepicker",e),e.settings.disabled&&this._disableDatepicker(t))},_attachments:function(t,e){var i,s=this._get(e,"appendText"),n=this._get(e,"isRTL");e.append&&e.append.remove(),s&&(e.append=V("<span>").addClass(this._appendClass).text(s),t[n?"before":"after"](e.append)),t.off("focus",this._showDatepicker),e.trigger&&e.trigger.remove(),"focus"!==(i=this._get(e,"showOn"))&&"both"!==i||t.on("focus",this._showDatepicker),"button"!==i&&"both"!==i||(s=this._get(e,"buttonText"),i=this._get(e,"buttonImage"),this._get(e,"buttonImageOnly")?e.trigger=V("<img>").addClass(this._triggerClass).attr({src:i,alt:s,title:s}):(e.trigger=V("<button type='button'>").addClass(this._triggerClass),i?e.trigger.html(V("<img>").attr({src:i,alt:s,title:s})):e.trigger.text(s)),t[n?"before":"after"](e.trigger),e.trigger.on("click",function(){return V.datepicker._datepickerShowing&&V.datepicker._lastInput===t[0]?V.datepicker._hideDatepicker():(V.datepicker._datepickerShowing&&V.datepicker._lastInput!==t[0]&&V.datepicker._hideDatepicker(),V.datepicker._showDatepicker(t[0])),!1}))},_autoSize:function(t){var e,i,s,n,o,a;this._get(t,"autoSize")&&!t.inline&&(o=new Date(2009,11,20),(a=this._get(t,"dateFormat")).match(/[DM]/)&&(e=function(t){for(n=s=i=0;n<t.length;n++)t[n].length>i&&(i=t[n].length,s=n);return s},o.setMonth(e(this._get(t,a.match(/MM/)?"monthNames":"monthNamesShort"))),o.setDate(e(this._get(t,a.match(/DD/)?"dayNames":"dayNamesShort"))+20-o.getDay())),t.input.attr("size",this._formatDate(t,o).length))},_inlineDatepicker:function(t,e){var i=V(t);i.hasClass(this.markerClassName)||(i.addClass(this.markerClassName).append(e.dpDiv),V.data(t,"datepicker",e),this._setDate(e,this._getDefaultDate(e),!0),this._updateDatepicker(e),this._updateAlternate(e),e.settings.disabled&&this._disableDatepicker(t),e.dpDiv.css("display","block"))},_dialogDatepicker:function(t,e,i,s,n){var o,a=this._dialogInst;return a||(this.uuid+=1,o="dp"+this.uuid,this._dialogInput=V("<input type='text' id='"+o+"' style='position: absolute; top: -100px; width: 0px;'/>"),this._dialogInput.on("keydown",this._doKeyDown),V("body").append(this._dialogInput),(a=this._dialogInst=this._newInst(this._dialogInput,!1)).settings={},V.data(this._dialogInput[0],"datepicker",a)),at(a.settings,s||{}),e=e&&e.constructor===Date?this._formatDate(a,e):e,this._dialogInput.val(e),this._pos=n?n.length?n:[n.pageX,n.pageY]:null,this._pos||(o=document.documentElement.clientWidth,s=document.documentElement.clientHeight,e=document.documentElement.scrollLeft||document.body.scrollLeft,n=document.documentElement.scrollTop||document.body.scrollTop,this._pos=[o/2-100+e,s/2-150+n]),this._dialogInput.css("left",this._pos[0]+20+"px").css("top",this._pos[1]+"px"),a.settings.onSelect=i,this._inDialog=!0,this.dpDiv.addClass(this._dialogClass),this._showDatepicker(this._dialogInput[0]),V.blockUI&&V.blockUI(this.dpDiv),V.data(this._dialogInput[0],"datepicker",a),this},_destroyDatepicker:function(t){var e,i=V(t),s=V.data(t,"datepicker");i.hasClass(this.markerClassName)&&(e=t.nodeName.toLowerCase(),V.removeData(t,"datepicker"),"input"===e?(s.append.remove(),s.trigger.remove(),i.removeClass(this.markerClassName).off("focus",this._showDatepicker).off("keydown",this._doKeyDown).off("keypress",this._doKeyPress).off("keyup",this._doKeyUp)):"div"!==e&&"span"!==e||i.removeClass(this.markerClassName).empty(),it===s&&(it=null,this._curInst=null))},_enableDatepicker:function(e){var t,i=V(e),s=V.data(e,"datepicker");i.hasClass(this.markerClassName)&&("input"===(t=e.nodeName.toLowerCase())?(e.disabled=!1,s.trigger.filter("button").each(function(){this.disabled=!1}).end().filter("img").css({opacity:"1.0",cursor:""})):"div"!==t&&"span"!==t||((i=i.children("."+this._inlineClass)).children().removeClass("ui-state-disabled"),i.find("select.ui-datepicker-month, select.ui-datepicker-year").prop("disabled",!1)),this._disabledInputs=V.map(this._disabledInputs,function(t){return t===e?null:t}))},_disableDatepicker:function(e){var t,i=V(e),s=V.data(e,"datepicker");i.hasClass(this.markerClassName)&&("input"===(t=e.nodeName.toLowerCase())?(e.disabled=!0,s.trigger.filter("button").each(function(){this.disabled=!0}).end().filter("img").css({opacity:"0.5",cursor:"default"})):"div"!==t&&"span"!==t||((i=i.children("."+this._inlineClass)).children().addClass("ui-state-disabled"),i.find("select.ui-datepicker-month, select.ui-datepicker-year").prop("disabled",!0)),this._disabledInputs=V.map(this._disabledInputs,function(t){return t===e?null:t}),this._disabledInputs[this._disabledInputs.length]=e)},_isDisabledDatepicker:function(t){if(!t)return!1;for(var e=0;e<this._disabledInputs.length;e++)if(this._disabledInputs[e]===t)return!0;return!1},_getInst:function(t){try{return V.data(t,"datepicker")}catch(t){throw"Missing instance data for this datepicker"}},_optionDatepicker:function(t,e,i){var s,n,o=this._getInst(t);if(2===arguments.length&&"string"==typeof e)return"defaults"===e?V.extend({},V.datepicker._defaults):o?"all"===e?V.extend({},o.settings):this._get(o,e):null;s=e||{},"string"==typeof e&&((s={})[e]=i),o&&(this._curInst===o&&this._hideDatepicker(),n=this._getDateDatepicker(t,!0),e=this._getMinMaxDate(o,"min"),i=this._getMinMaxDate(o,"max"),at(o.settings,s),null!==e&&void 0!==s.dateFormat&&void 0===s.minDate&&(o.settings.minDate=this._formatDate(o,e)),null!==i&&void 0!==s.dateFormat&&void 0===s.maxDate&&(o.settings.maxDate=this._formatDate(o,i)),"disabled"in s&&(s.disabled?this._disableDatepicker(t):this._enableDatepicker(t)),this._attachments(V(t),o),this._autoSize(o),this._setDate(o,n),this._updateAlternate(o),this._updateDatepicker(o))},_changeDatepicker:function(t,e,i){this._optionDatepicker(t,e,i)},_refreshDatepicker:function(t){t=this._getInst(t);t&&this._updateDatepicker(t)},_setDateDatepicker:function(t,e){t=this._getInst(t);t&&(this._setDate(t,e),this._updateDatepicker(t),this._updateAlternate(t))},_getDateDatepicker:function(t,e){t=this._getInst(t);return t&&!t.inline&&this._setDateFromField(t,e),t?this._getDate(t):null},_doKeyDown:function(t){var e,i,s=V.datepicker._getInst(t.target),n=!0,o=s.dpDiv.is(".ui-datepicker-rtl");if(s._keyEvent=!0,V.datepicker._datepickerShowing)switch(t.keyCode){case 9:V.datepicker._hideDatepicker(),n=!1;break;case 13:return(i=V("td."+V.datepicker._dayOverClass+":not(."+V.datepicker._currentClass+")",s.dpDiv))[0]&&V.datepicker._selectDay(t.target,s.selectedMonth,s.selectedYear,i[0]),(e=V.datepicker._get(s,"onSelect"))?(i=V.datepicker._formatDate(s),e.apply(s.input?s.input[0]:null,[i,s])):V.datepicker._hideDatepicker(),!1;case 27:V.datepicker._hideDatepicker();break;case 33:V.datepicker._adjustDate(t.target,t.ctrlKey?-V.datepicker._get(s,"stepBigMonths"):-V.datepicker._get(s,"stepMonths"),"M");break;case 34:V.datepicker._adjustDate(t.target,t.ctrlKey?+V.datepicker._get(s,"stepBigMonths"):+V.datepicker._get(s,"stepMonths"),"M");break;case 35:(t.ctrlKey||t.metaKey)&&V.datepicker._clearDate(t.target),n=t.ctrlKey||t.metaKey;break;case 36:(t.ctrlKey||t.metaKey)&&V.datepicker._gotoToday(t.target),n=t.ctrlKey||t.metaKey;break;case 37:(t.ctrlKey||t.metaKey)&&V.datepicker._adjustDate(t.target,o?1:-1,"D"),n=t.ctrlKey||t.metaKey,t.originalEvent.altKey&&V.datepicker._adjustDate(t.target,t.ctrlKey?-V.datepicker._get(s,"stepBigMonths"):-V.datepicker._get(s,"stepMonths"),"M");break;case 38:(t.ctrlKey||t.metaKey)&&V.datepicker._adjustDate(t.target,-7,"D"),n=t.ctrlKey||t.metaKey;break;case 39:(t.ctrlKey||t.metaKey)&&V.datepicker._adjustDate(t.target,o?-1:1,"D"),n=t.ctrlKey||t.metaKey,t.originalEvent.altKey&&V.datepicker._adjustDate(t.target,t.ctrlKey?+V.datepicker._get(s,"stepBigMonths"):+V.datepicker._get(s,"stepMonths"),"M");break;case 40:(t.ctrlKey||t.metaKey)&&V.datepicker._adjustDate(t.target,7,"D"),n=t.ctrlKey||t.metaKey;break;default:n=!1}else 36===t.keyCode&&t.ctrlKey?V.datepicker._showDatepicker(this):n=!1;n&&(t.preventDefault(),t.stopPropagation())},_doKeyPress:function(t){var e,i=V.datepicker._getInst(t.target);if(V.datepicker._get(i,"constrainInput"))return e=V.datepicker._possibleChars(V.datepicker._get(i,"dateFormat")),i=String.fromCharCode(null==t.charCode?t.keyCode:t.charCode),t.ctrlKey||t.metaKey||i<" "||!e||-1<e.indexOf(i)},_doKeyUp:function(t){t=V.datepicker._getInst(t.target);if(t.input.val()!==t.lastVal)try{V.datepicker.parseDate(V.datepicker._get(t,"dateFormat"),t.input?t.input.val():null,V.datepicker._getFormatConfig(t))&&(V.datepicker._setDateFromField(t),V.datepicker._updateAlternate(t),V.datepicker._updateDatepicker(t))}catch(t){}return!0},_showDatepicker:function(t){var e,i,s,n;"input"!==(t=t.target||t).nodeName.toLowerCase()&&(t=V("input",t.parentNode)[0]),V.datepicker._isDisabledDatepicker(t)||V.datepicker._lastInput===t||(n=V.datepicker._getInst(t),V.datepicker._curInst&&V.datepicker._curInst!==n&&(V.datepicker._curInst.dpDiv.stop(!0,!0),n&&V.datepicker._datepickerShowing&&V.datepicker._hideDatepicker(V.datepicker._curInst.input[0])),!1!==(i=(s=V.datepicker._get(n,"beforeShow"))?s.apply(t,[t,n]):{})&&(at(n.settings,i),n.lastVal=null,V.datepicker._lastInput=t,V.datepicker._setDateFromField(n),V.datepicker._inDialog&&(t.value=""),V.datepicker._pos||(V.datepicker._pos=V.datepicker._findPos(t),V.datepicker._pos[1]+=t.offsetHeight),e=!1,V(t).parents().each(function(){return!(e|="fixed"===V(this).css("position"))}),s={left:V.datepicker._pos[0],top:V.datepicker._pos[1]},V.datepicker._pos=null,n.dpDiv.empty(),n.dpDiv.css({position:"absolute",display:"block",top:"-1000px"}),V.datepicker._updateDatepicker(n),s=V.datepicker._checkOffset(n,s,e),n.dpDiv.css({position:V.datepicker._inDialog&&V.blockUI?"static":e?"fixed":"absolute",display:"none",left:s.left+"px",top:s.top+"px"}),n.inline||(i=V.datepicker._get(n,"showAnim"),s=V.datepicker._get(n,"duration"),n.dpDiv.css("z-index",function(t){for(var e,i;t.length&&t[0]!==document;){if(("absolute"===(e=t.css("position"))||"relative"===e||"fixed"===e)&&(i=parseInt(t.css("zIndex"),10),!isNaN(i)&&0!==i))return i;t=t.parent()}return 0}(V(t))+1),V.datepicker._datepickerShowing=!0,V.effects&&V.effects.effect[i]?n.dpDiv.show(i,V.datepicker._get(n,"showOptions"),s):n.dpDiv[i||"show"](i?s:null),V.datepicker._shouldFocusInput(n)&&n.input.trigger("focus"),V.datepicker._curInst=n)))},_updateDatepicker:function(t){this.maxRows=4,(it=t).dpDiv.empty().append(this._generateHTML(t)),this._attachHandlers(t);var e,i=this._getNumberOfMonths(t),s=i[1],n=t.dpDiv.find("."+this._dayOverClass+" a"),o=V.datepicker._get(t,"onUpdateDatepicker");0<n.length&&ot.apply(n.get(0)),t.dpDiv.removeClass("ui-datepicker-multi-2 ui-datepicker-multi-3 ui-datepicker-multi-4").width(""),1<s&&t.dpDiv.addClass("ui-datepicker-multi-"+s).css("width",17*s+"em"),t.dpDiv[(1!==i[0]||1!==i[1]?"add":"remove")+"Class"]("ui-datepicker-multi"),t.dpDiv[(this._get(t,"isRTL")?"add":"remove")+"Class"]("ui-datepicker-rtl"),t===V.datepicker._curInst&&V.datepicker._datepickerShowing&&V.datepicker._shouldFocusInput(t)&&t.input.trigger("focus"),t.yearshtml&&(e=t.yearshtml,setTimeout(function(){e===t.yearshtml&&t.yearshtml&&t.dpDiv.find("select.ui-datepicker-year").first().replaceWith(t.yearshtml),e=t.yearshtml=null},0)),o&&o.apply(t.input?t.input[0]:null,[t])},_shouldFocusInput:function(t){return t.input&&t.input.is(":visible")&&!t.input.is(":disabled")&&!t.input.is(":focus")},_checkOffset:function(t,e,i){var s=t.dpDiv.outerWidth(),n=t.dpDiv.outerHeight(),o=t.input?t.input.outerWidth():0,a=t.input?t.input.outerHeight():0,r=document.documentElement.clientWidth+(i?0:V(document).scrollLeft()),l=document.documentElement.clientHeight+(i?0:V(document).scrollTop());return e.left-=this._get(t,"isRTL")?s-o:0,e.left-=i&&e.left===t.input.offset().left?V(document).scrollLeft():0,e.top-=i&&e.top===t.input.offset().top+a?V(document).scrollTop():0,e.left-=Math.min(e.left,e.left+s>r&&s<r?Math.abs(e.left+s-r):0),e.top-=Math.min(e.top,e.top+n>l&&n<l?Math.abs(n+a):0),e},_findPos:function(t){for(var e=this._getInst(t),i=this._get(e,"isRTL");t&&("hidden"===t.type||1!==t.nodeType||V.expr.pseudos.hidden(t));)t=t[i?"previousSibling":"nextSibling"];return[(e=V(t).offset()).left,e.top]},_hideDatepicker:function(t){var e,i,s=this._curInst;!s||t&&s!==V.data(t,"datepicker")||this._datepickerShowing&&(e=this._get(s,"showAnim"),i=this._get(s,"duration"),t=function(){V.datepicker._tidyDialog(s)},V.effects&&(V.effects.effect[e]||V.effects[e])?s.dpDiv.hide(e,V.datepicker._get(s,"showOptions"),i,t):s.dpDiv["slideDown"===e?"slideUp":"fadeIn"===e?"fadeOut":"hide"](e?i:null,t),e||t(),this._datepickerShowing=!1,(t=this._get(s,"onClose"))&&t.apply(s.input?s.input[0]:null,[s.input?s.input.val():"",s]),this._lastInput=null,this._inDialog&&(this._dialogInput.css({position:"absolute",left:"0",top:"-100px"}),V.blockUI&&(V.unblockUI(),V("body").append(this.dpDiv))),this._inDialog=!1)},_tidyDialog:function(t){t.dpDiv.removeClass(this._dialogClass).off(".ui-datepicker-calendar")},_checkExternalClick:function(t){var e;V.datepicker._curInst&&(e=V(t.target),t=V.datepicker._getInst(e[0]),(e[0].id===V.datepicker._mainDivId||0!==e.parents("#"+V.datepicker._mainDivId).length||e.hasClass(V.datepicker.markerClassName)||e.closest("."+V.datepicker._triggerClass).length||!V.datepicker._datepickerShowing||V.datepicker._inDialog&&V.blockUI)&&(!e.hasClass(V.datepicker.markerClassName)||V.datepicker._curInst===t)||V.datepicker._hideDatepicker())},_adjustDate:function(t,e,i){var s=V(t),t=this._getInst(s[0]);this._isDisabledDatepicker(s[0])||(this._adjustInstDate(t,e,i),this._updateDatepicker(t))},_gotoToday:function(t){var e=V(t),i=this._getInst(e[0]);this._get(i,"gotoCurrent")&&i.currentDay?(i.selectedDay=i.currentDay,i.drawMonth=i.selectedMonth=i.currentMonth,i.drawYear=i.selectedYear=i.currentYear):(t=new Date,i.selectedDay=t.getDate(),i.drawMonth=i.selectedMonth=t.getMonth(),i.drawYear=i.selectedYear=t.getFullYear()),this._notifyChange(i),this._adjustDate(e)},_selectMonthYear:function(t,e,i){var s=V(t),t=this._getInst(s[0]);t["selected"+("M"===i?"Month":"Year")]=t["draw"+("M"===i?"Month":"Year")]=parseInt(e.options[e.selectedIndex].value,10),this._notifyChange(t),this._adjustDate(s)},_selectDay:function(t,e,i,s){var n=V(t);V(s).hasClass(this._unselectableClass)||this._isDisabledDatepicker(n[0])||((n=this._getInst(n[0])).selectedDay=n.currentDay=parseInt(V("a",s).attr("data-date")),n.selectedMonth=n.currentMonth=e,n.selectedYear=n.currentYear=i,this._selectDate(t,this._formatDate(n,n.currentDay,n.currentMonth,n.currentYear)))},_clearDate:function(t){t=V(t);this._selectDate(t,"")},_selectDate:function(t,e){var i=V(t),t=this._getInst(i[0]);e=null!=e?e:this._formatDate(t),t.input&&t.input.val(e),this._updateAlternate(t),(i=this._get(t,"onSelect"))?i.apply(t.input?t.input[0]:null,[e,t]):t.input&&t.input.trigger("change"),t.inline?this._updateDatepicker(t):(this._hideDatepicker(),this._lastInput=t.input[0],"object"!=typeof t.input[0]&&t.input.trigger("focus"),this._lastInput=null)},_updateAlternate:function(t){var e,i,s=this._get(t,"altField");s&&(e=this._get(t,"altFormat")||this._get(t,"dateFormat"),i=this._getDate(t),t=this.formatDate(e,i,this._getFormatConfig(t)),V(document).find(s).val(t))},noWeekends:function(t){t=t.getDay();return[0<t&&t<6,""]},iso8601Week:function(t){var e=new Date(t.getTime());return e.setDate(e.getDate()+4-(e.getDay()||7)),t=e.getTime(),e.setMonth(0),e.setDate(1),Math.floor(Math.round((t-e)/864e5)/7)+1},parseDate:function(e,n,t){if(null==e||null==n)throw"Invalid arguments";if(""===(n="object"==typeof n?n.toString():n+""))return null;for(var i,s,o,a=0,r=(t?t.shortYearCutoff:null)||this._defaults.shortYearCutoff,r="string"!=typeof r?r:(new Date).getFullYear()%100+parseInt(r,10),l=(t?t.dayNamesShort:null)||this._defaults.dayNamesShort,h=(t?t.dayNames:null)||this._defaults.dayNames,c=(t?t.monthNamesShort:null)||this._defaults.monthNamesShort,u=(t?t.monthNames:null)||this._defaults.monthNames,d=-1,p=-1,f=-1,g=-1,m=!1,_=function(t){t=w+1<e.length&&e.charAt(w+1)===t;return t&&w++,t},v=function(t){var e=_(t),e="@"===t?14:"!"===t?20:"y"===t&&e?4:"o"===t?3:2,e=new RegExp("^\\d{"+("y"===t?e:1)+","+e+"}"),e=n.substring(a).match(e);if(!e)throw"Missing number at position "+a;return a+=e[0].length,parseInt(e[0],10)},b=function(t,e,i){var s=-1,e=V.map(_(t)?i:e,function(t,e){return[[e,t]]}).sort(function(t,e){return-(t[1].length-e[1].length)});if(V.each(e,function(t,e){var i=e[1];if(n.substr(a,i.length).toLowerCase()===i.toLowerCase())return s=e[0],a+=i.length,!1}),-1!==s)return s+1;throw"Unknown name at position "+a},y=function(){if(n.charAt(a)!==e.charAt(w))throw"Unexpected literal at position 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V.datepicker._selectMonthYear(i,this,"M"),!1},selectYear:function(){return V.datepicker._selectMonthYear(i,this,"Y"),!1}};V(this).on(this.getAttribute("data-event"),t[this.getAttribute("data-handler")])})},_generateHTML:function(t){var e,i,s,n,o,a,r,l,h,c,u,d,p,f,g,m,_,v,b,y,w,x,k,C,D,I,T,P,M,S,H,z,A=new Date,O=this._daylightSavingAdjust(new Date(A.getFullYear(),A.getMonth(),A.getDate())),N=this._get(t,"isRTL"),E=this._get(t,"showButtonPanel"),W=this._get(t,"hideIfNoPrevNext"),F=this._get(t,"navigationAsDateFormat"),L=this._getNumberOfMonths(t),R=this._get(t,"showCurrentAtPos"),A=this._get(t,"stepMonths"),Y=1!==L[0]||1!==L[1],B=this._daylightSavingAdjust(t.currentDay?new Date(t.currentYear,t.currentMonth,t.currentDay):new Date(9999,9,9)),j=this._getMinMaxDate(t,"min"),q=this._getMinMaxDate(t,"max"),K=t.drawMonth-R,U=t.drawYear;if(K<0&&(K+=12,U--),q)for(e=this._daylightSavingAdjust(new 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ui-helper-clearfix"+y+"'>"+(/all|left/.test(y)&&0===m?N?s:i:"")+(/all|right/.test(y)&&0===m?N?i:s:"")+this._generateMonthYearHeader(t,K,U,j,q,0<m||0<v,l,h)+"</div><table class='ui-datepicker-calendar'><thead><tr>",x=o?"<th class='ui-datepicker-week-col'>"+this._get(t,"weekHeader")+"</th>":"",g=0;g<7;g++)x+="<th scope='col'"+(5<=(g+n+6)%7?" class='ui-datepicker-week-end'":"")+"><span title='"+a[k=(g+n)%7]+"'>"+r[k]+"</span></th>";for(w+=x+"</tr></thead><tbody>",D=this._getDaysInMonth(U,K),U===t.selectedYear&&K===t.selectedMonth&&(t.selectedDay=Math.min(t.selectedDay,D)),C=(this._getFirstDayOfMonth(U,K)-n+7)%7,D=Math.ceil((C+D)/7),I=Y&&this.maxRows>D?this.maxRows:D,this.maxRows=I,T=this._daylightSavingAdjust(new Date(U,K,1-C)),P=0;P<I;P++){for(w+="<tr>",M=o?"<td class='ui-datepicker-week-col'>"+this._get(t,"calculateWeek")(T)+"</td>":"",g=0;g<7;g++)S=c?c.apply(t.input?t.input[0]:null,[T]):[!0,""],z=(H=T.getMonth()!==K)&&!d||!S[0]||j&&T<j||q&&q<T,M+="<td class='"+(5<=(g+n+6)%7?" ui-datepicker-week-end":"")+(H?" ui-datepicker-other-month":"")+(T.getTime()===b.getTime()&&K===t.selectedMonth&&t._keyEvent||p.getTime()===T.getTime()&&p.getTime()===b.getTime()?" 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l,h,c,u,d,p,f=this._get(t,"changeMonth"),g=this._get(t,"changeYear"),m=this._get(t,"showMonthAfterYear"),_=this._get(t,"selectMonthLabel"),v=this._get(t,"selectYearLabel"),b="<div class='ui-datepicker-title'>",y="";if(o||!f)y+="<span class='ui-datepicker-month'>"+a[e]+"</span>";else{for(l=s&&s.getFullYear()===i,h=n&&n.getFullYear()===i,y+="<select class='ui-datepicker-month' aria-label='"+_+"' data-handler='selectMonth' data-event='change'>",c=0;c<12;c++)(!l||c>=s.getMonth())&&(!h||c<=n.getMonth())&&(y+="<option value='"+c+"'"+(c===e?" selected='selected'":"")+">"+r[c]+"</option>");y+="</select>"}if(m||(b+=y+(!o&&f&&g?"":"&#xa0;")),!t.yearshtml)if(t.yearshtml="",o||!g)b+="<span class='ui-datepicker-year'>"+i+"</span>";else{for(a=this._get(t,"yearRange").split(":"),u=(new Date).getFullYear(),d=(_=function(t){t=t.match(/c[+\-].*/)?i+parseInt(t.substring(1),10):t.match(/[+\-].*/)?u+parseInt(t,10):parseInt(t,10);return 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t&&t<e?t:e},_notifyChange:function(t){var e=this._get(t,"onChangeMonthYear");e&&e.apply(t.input?t.input[0]:null,[t.selectedYear,t.selectedMonth+1,t])},_getNumberOfMonths:function(t){t=this._get(t,"numberOfMonths");return null==t?[1,1]:"number"==typeof t?[1,t]:t},_getMinMaxDate:function(t,e){return this._determineDate(t,this._get(t,e+"Date"),null)},_getDaysInMonth:function(t,e){return 32-this._daylightSavingAdjust(new Date(t,e,32)).getDate()},_getFirstDayOfMonth:function(t,e){return new Date(t,e,1).getDay()},_canAdjustMonth:function(t,e,i,s){var n=this._getNumberOfMonths(t),n=this._daylightSavingAdjust(new Date(i,s+(e<0?e:n[0]*n[1]),1));return e<0&&n.setDate(this._getDaysInMonth(n.getFullYear(),n.getMonth())),this._isInRange(t,n)},_isInRange:function(t,e){var i=this._getMinMaxDate(t,"min"),s=this._getMinMaxDate(t,"max"),n=null,o=null,a=this._get(t,"yearRange");return a&&(t=a.split(":"),a=(new 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this;V.datepicker.initialized||(V(document).on("mousedown",V.datepicker._checkExternalClick),V.datepicker.initialized=!0),0===V("#"+V.datepicker._mainDivId).length&&V("body").append(V.datepicker.dpDiv);var e=Array.prototype.slice.call(arguments,1);return"string"==typeof t&&("isDisabled"===t||"getDate"===t||"widget"===t)||"option"===t&&2===arguments.length&&"string"==typeof arguments[1]?V.datepicker["_"+t+"Datepicker"].apply(V.datepicker,[this[0]].concat(e)):this.each(function(){"string"==typeof t?V.datepicker["_"+t+"Datepicker"].apply(V.datepicker,[this].concat(e)):V.datepicker._attachDatepicker(this,t)})},V.datepicker=new st,V.datepicker.initialized=!1,V.datepicker.uuid=(new Date).getTime(),V.datepicker.version="1.13.2";V.datepicker,V.ui.ie=!!/msie [\w.]+/.exec(navigator.userAgent.toLowerCase());var rt=!1;V(document).on("mouseup",function(){rt=!1});V.widget("ui.mouse",{version:"1.13.2",options:{cancel:"input, textarea, button, select, 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i&&!s&&this._mouseCapture(t)?(this.mouseDelayMet=!this.options.delay,this.mouseDelayMet||(this._mouseDelayTimer=setTimeout(function(){e.mouseDelayMet=!0},this.options.delay)),this._mouseDistanceMet(t)&&this._mouseDelayMet(t)&&(this._mouseStarted=!1!==this._mouseStart(t),!this._mouseStarted)?(t.preventDefault(),!0):(!0===V.data(t.target,this.widgetName+".preventClickEvent")&&V.removeData(t.target,this.widgetName+".preventClickEvent"),this._mouseMoveDelegate=function(t){return e._mouseMove(t)},this._mouseUpDelegate=function(t){return e._mouseUp(t)},this.document.on("mousemove."+this.widgetName,this._mouseMoveDelegate).on("mouseup."+this.widgetName,this._mouseUpDelegate),t.preventDefault(),rt=!0)):!0}},_mouseMove:function(t){if(this._mouseMoved){if(V.ui.ie&&(!document.documentMode||document.documentMode<9)&&!t.button)return this._mouseUp(t);if(!t.which)if(t.originalEvent.altKey||t.originalEvent.ctrlKey||t.originalEvent.metaKey||t.originalEvent.shiftKey)this.ignoreMissingWhich=!0;else if(!this.ignoreMissingWhich)return this._mouseUp(t)}return(t.which||t.button)&&(this._mouseMoved=!0),this._mouseStarted?(this._mouseDrag(t),t.preventDefault()):(this._mouseDistanceMet(t)&&this._mouseDelayMet(t)&&(this._mouseStarted=!1!==this._mouseStart(this._mouseDownEvent,t),this._mouseStarted?this._mouseDrag(t):this._mouseUp(t)),!this._mouseStarted)},_mouseUp:function(t){this.document.off("mousemove."+this.widgetName,this._mouseMoveDelegate).off("mouseup."+this.widgetName,this._mouseUpDelegate),this._mouseStarted&&(this._mouseStarted=!1,t.target===this._mouseDownEvent.target&&V.data(t.target,this.widgetName+".preventClickEvent",!0),this._mouseStop(t)),this._mouseDelayTimer&&(clearTimeout(this._mouseDelayTimer),delete this._mouseDelayTimer),this.ignoreMissingWhich=!1,rt=!1,t.preventDefault()},_mouseDistanceMet:function(t){return Math.max(Math.abs(this._mouseDownEvent.pageX-t.pageX),Math.abs(this._mouseDownEvent.pageY-t.pageY))>=this.options.distance},_mouseDelayMet:function(){return this.mouseDelayMet},_mouseStart:function(){},_mouseDrag:function(){},_mouseStop:function(){},_mouseCapture:function(){return!0}}),V.ui.plugin={add:function(t,e,i){var s,n=V.ui[t].prototype;for(s in i)n.plugins[s]=n.plugins[s]||[],n.plugins[s].push([e,i[s]])},call:function(t,e,i,s){var n,o=t.plugins[e];if(o&&(s||t.element[0].parentNode&&11!==t.element[0].parentNode.nodeType))for(n=0;n<o.length;n++)t.options[o[n][0]]&&o[n][1].apply(t.element,i)}},V.ui.safeBlur=function(t){t&&"body"!==t.nodeName.toLowerCase()&&V(t).trigger("blur")};V.widget("ui.draggable",V.ui.mouse,{version:"1.13.2",widgetEventPrefix:"drag",options:{addClasses:!0,appendTo:"parent",axis:!1,connectToSortable:!1,containment:!1,cursor:"auto",cursorAt:!1,grid:!1,handle:!1,helper:"original",iframeFix:!1,opacity:!1,refreshPositions:!1,revert:!1,revertDuration:500,scope:"default",scroll:!0,scrollSensitivity:20,scrollSpeed:20,snap:!1,snapMode:"both",snapTolerance:20,stack:!1,zIndex:!1,drag:null,start:null,stop:null},_create:function(){"original"===this.options.helper&&this._setPositionRelative(),this.options.addClasses&&this._addClass("ui-draggable"),this._setHandleClassName(),this._mouseInit()},_setOption:function(t,e){this._super(t,e),"handle"===t&&(this._removeHandleClassName(),this._setHandleClassName())},_destroy:function(){(this.helper||this.element).is(".ui-draggable-dragging")?this.destroyOnClear=!0:(this._removeHandleClassName(),this._mouseDestroy())},_mouseCapture:function(t){var e=this.options;return!(this.helper||e.disabled||0<V(t.target).closest(".ui-resizable-handle").length)&&(this.handle=this._getHandle(t),!!this.handle&&(this._blurActiveElement(t),this._blockFrames(!0===e.iframeFix?"iframe":e.iframeFix),!0))},_blockFrames:function(t){this.iframeBlocks=this.document.find(t).map(function(){var t=V(this);return V("<div>").css("position","absolute").appendTo(t.parent()).outerWidth(t.outerWidth()).outerHeight(t.outerHeight()).offset(t.offset())[0]})},_unblockFrames:function(){this.iframeBlocks&&(this.iframeBlocks.remove(),delete this.iframeBlocks)},_blurActiveElement:function(t){var e=V.ui.safeActiveElement(this.document[0]);V(t.target).closest(e).length||V.ui.safeBlur(e)},_mouseStart:function(t){var e=this.options;return this.helper=this._createHelper(t),this._addClass(this.helper,"ui-draggable-dragging"),this._cacheHelperProportions(),V.ui.ddmanager&&(V.ui.ddmanager.current=this),this._cacheMargins(),this.cssPosition=this.helper.css("position"),this.scrollParent=this.helper.scrollParent(!0),this.offsetParent=this.helper.offsetParent(),this.hasFixedAncestor=0<this.helper.parents().filter(function(){return"fixed"===V(this).css("position")}).length,this.positionAbs=this.element.offset(),this._refreshOffsets(t),this.originalPosition=this.position=this._generatePosition(t,!1),this.originalPageX=t.pageX,this.originalPageY=t.pageY,e.cursorAt&&this._adjustOffsetFromHelper(e.cursorAt),this._setContainment(),!1===this._trigger("start",t)?(this._clear(),!1):(this._cacheHelperProportions(),V.ui.ddmanager&&!e.dropBehaviour&&V.ui.ddmanager.prepareOffsets(this,t),this._mouseDrag(t,!0),V.ui.ddmanager&&V.ui.ddmanager.dragStart(this,t),!0)},_refreshOffsets:function(t){this.offset={top:this.positionAbs.top-this.margins.top,left:this.positionAbs.left-this.margins.left,scroll:!1,parent:this._getParentOffset(),relative:this._getRelativeOffset()},this.offset.click={left:t.pageX-this.offset.left,top:t.pageY-this.offset.top}},_mouseDrag:function(t,e){if(this.hasFixedAncestor&&(this.offset.parent=this._getParentOffset()),this.position=this._generatePosition(t,!0),this.positionAbs=this._convertPositionTo("absolute"),!e){e=this._uiHash();if(!1===this._trigger("drag",t,e))return 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this.helper.is(".ui-draggable-dragging")?this._mouseUp(new V.Event("mouseup",{target:this.element[0]})):this._clear(),this},_getHandle:function(t){return!this.options.handle||!!V(t.target).closest(this.element.find(this.options.handle)).length},_setHandleClassName:function(){this.handleElement=this.options.handle?this.element.find(this.options.handle):this.element,this._addClass(this.handleElement,"ui-draggable-handle")},_removeHandleClassName:function(){this._removeClass(this.handleElement,"ui-draggable-handle")},_createHelper:function(t){var e=this.options,i="function"==typeof e.helper,t=i?V(e.helper.apply(this.element[0],[t])):"clone"===e.helper?this.element.clone().removeAttr("id"):this.element;return t.parents("body").length||t.appendTo("parent"===e.appendTo?this.element[0].parentNode:e.appendTo),i&&t[0]===this.element[0]&&this._setPositionRelative(),t[0]===this.element[0]||/(fixed|absolute)/.test(t.css("position"))||t.css("position","absolute"),t},_setPositionRelative:function(){/^(?:r|a|f)/.test(this.element.css("position"))||(this.element[0].style.position="relative")},_adjustOffsetFromHelper:function(t){"string"==typeof t&&(t=t.split(" ")),"left"in(t=Array.isArray(t)?{left:+t[0],top:+t[1]||0}:t)&&(this.offset.click.left=t.left+this.margins.left),"right"in t&&(this.offset.click.left=this.helperProportions.width-t.right+this.margins.left),"top"in t&&(this.offset.click.top=t.top+this.margins.top),"bottom"in t&&(this.offset.click.top=this.helperProportions.height-t.bottom+this.margins.top)},_isRootNode:function(t){return/(html|body)/i.test(t.tagName)||t===this.document[0]},_getParentOffset:function(){var t=this.offsetParent.offset(),e=this.document[0];return"absolute"===this.cssPosition&&this.scrollParent[0]!==e&&V.contains(this.scrollParent[0],this.offsetParent[0])&&(t.left+=this.scrollParent.scrollLeft(),t.top+=this.scrollParent.scrollTop()),{top:(t=this._isRootNode(this.offsetParent[0])?{top:0,left:0}:t).top+(parseInt(this.offsetParent.css("borderTopWidth"),10)||0),left:t.left+(parseInt(this.offsetParent.css("borderLeftWidth"),10)||0)}},_getRelativeOffset:function(){if("relative"!==this.cssPosition)return{top:0,left:0};var t=this.element.position(),e=this._isRootNode(this.scrollParent[0]);return{top:t.top-(parseInt(this.helper.css("top"),10)||0)+(e?0:this.scrollParent.scrollTop()),left:t.left-(parseInt(this.helper.css("left"),10)||0)+(e?0:this.scrollParent.scrollLeft())}},_cacheMargins:function(){this.margins={left:parseInt(this.element.css("marginLeft"),10)||0,top:parseInt(this.element.css("marginTop"),10)||0,right:parseInt(this.element.css("marginRight"),10)||0,bottom:parseInt(this.element.css("marginBottom"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var t,e,i,s=this.options,n=this.document[0];this.relativeContainer=null,s.containment?"window"!==s.containment?"document"!==s.containment?s.containment.constructor!==Array?("parent"===s.containment&&(s.containment=this.helper[0].parentNode),(i=(e=V(s.containment))[0])&&(t=/(scroll|auto)/.test(e.css("overflow")),this.containment=[(parseInt(e.css("borderLeftWidth"),10)||0)+(parseInt(e.css("paddingLeft"),10)||0),(parseInt(e.css("borderTopWidth"),10)||0)+(parseInt(e.css("paddingTop"),10)||0),(t?Math.max(i.scrollWidth,i.offsetWidth):i.offsetWidth)-(parseInt(e.css("borderRightWidth"),10)||0)-(parseInt(e.css("paddingRight"),10)||0)-this.helperProportions.width-this.margins.left-this.margins.right,(t?Math.max(i.scrollHeight,i.offsetHeight):i.offsetHeight)-(parseInt(e.css("borderBottomWidth"),10)||0)-(parseInt(e.css("paddingBottom"),10)||0)-this.helperProportions.height-this.margins.top-this.margins.bottom],this.relativeContainer=e)):this.containment=s.containment:this.containment=[0,0,V(n).width()-this.helperProportions.width-this.margins.left,(V(n).height()||n.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top]:this.containment=[V(window).scrollLeft()-this.offset.relative.left-this.offset.parent.left,V(window).scrollTop()-this.offset.relative.top-this.offset.parent.top,V(window).scrollLeft()+V(window).width()-this.helperProportions.width-this.margins.left,V(window).scrollTop()+(V(window).height()||n.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top]:this.containment=null},_convertPositionTo:function(t,e){e=e||this.position;var i="absolute"===t?1:-1,t=this._isRootNode(this.scrollParent[0]);return{top:e.top+this.offset.relative.top*i+this.offset.parent.top*i-("fixed"===this.cssPosition?-this.offset.scroll.top:t?0:this.offset.scroll.top)*i,left:e.left+this.offset.relative.left*i+this.offset.parent.left*i-("fixed"===this.cssPosition?-this.offset.scroll.left:t?0:this.offset.scroll.left)*i}},_generatePosition:function(t,e){var i,s=this.options,n=this._isRootNode(this.scrollParent[0]),o=t.pageX,a=t.pageY;return 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i=i||this._uiHash(),V.ui.plugin.call(this,t,[e,i,this],!0),/^(drag|start|stop)/.test(t)&&(this.positionAbs=this._convertPositionTo("absolute"),i.offset=this.positionAbs),V.Widget.prototype._trigger.call(this,t,e,i)},plugins:{},_uiHash:function(){return{helper:this.helper,position:this.position,originalPosition:this.originalPosition,offset:this.positionAbs}}}),V.ui.plugin.add("draggable","connectToSortable",{start:function(e,t,i){var s=V.extend({},t,{item:i.element});i.sortables=[],V(i.options.connectToSortable).each(function(){var t=V(this).sortable("instance");t&&!t.options.disabled&&(i.sortables.push(t),t.refreshPositions(),t._trigger("activate",e,s))})},stop:function(e,t,i){var s=V.extend({},t,{item:i.element});i.cancelHelperRemoval=!1,V.each(i.sortables,function(){var t=this;t.isOver?(t.isOver=0,i.cancelHelperRemoval=!0,t.cancelHelperRemoval=!1,t._storedCSS={position:t.placeholder.css("position"),top:t.placeholder.css("top"),left:t.placeholder.css("left")},t._mouseStop(e),t.options.helper=t.options._helper):(t.cancelHelperRemoval=!0,t._trigger("deactivate",e,s))})},drag:function(i,s,n){V.each(n.sortables,function(){var t=!1,e=this;e.positionAbs=n.positionAbs,e.helperProportions=n.helperProportions,e.offset.click=n.offset.click,e._intersectsWith(e.containerCache)&&(t=!0,V.each(n.sortables,function(){return this.positionAbs=n.positionAbs,this.helperProportions=n.helperProportions,this.offset.click=n.offset.click,t=this!==e&&this._intersectsWith(this.containerCache)&&V.contains(e.element[0],this.element[0])?!1:t})),t?(e.isOver||(e.isOver=1,n._parent=s.helper.parent(),e.currentItem=s.helper.appendTo(e.element).data("ui-sortable-item",!0),e.options._helper=e.options.helper,e.options.helper=function(){return s.helper[0]},i.target=e.currentItem[0],e._mouseCapture(i,!0),e._mouseStart(i,!0,!0),e.offset.click.top=n.offset.click.top,e.offset.click.left=n.offset.click.left,e.offset.parent.left-=n.offset.parent.left-e.offset.parent.left,e.offset.parent.top-=n.offset.parent.top-e.offset.parent.top,n._trigger("toSortable",i),n.dropped=e.element,V.each(n.sortables,function(){this.refreshPositions()}),n.currentItem=n.element,e.fromOutside=n),e.currentItem&&(e._mouseDrag(i),s.position=e.position)):e.isOver&&(e.isOver=0,e.cancelHelperRemoval=!0,e.options._revert=e.options.revert,e.options.revert=!1,e._trigger("out",i,e._uiHash(e)),e._mouseStop(i,!0),e.options.revert=e.options._revert,e.options.helper=e.options._helper,e.placeholder&&e.placeholder.remove(),s.helper.appendTo(n._parent),n._refreshOffsets(i),s.position=n._generatePosition(i,!0),n._trigger("fromSortable",i),n.dropped=!1,V.each(n.sortables,function(){this.refreshPositions()}))})}}),V.ui.plugin.add("draggable","cursor",{start:function(t,e,i){var s=V("body"),i=i.options;s.css("cursor")&&(i._cursor=s.css("cursor")),s.css("cursor",i.cursor)},stop:function(t,e,i){i=i.options;i._cursor&&V("body").css("cursor",i._cursor)}}),V.ui.plugin.add("draggable","opacity",{start:function(t,e,i){e=V(e.helper),i=i.options;e.css("opacity")&&(i._opacity=e.css("opacity")),e.css("opacity",i.opacity)},stop:function(t,e,i){i=i.options;i._opacity&&V(e.helper).css("opacity",i._opacity)}}),V.ui.plugin.add("draggable","scroll",{start:function(t,e,i){i.scrollParentNotHidden||(i.scrollParentNotHidden=i.helper.scrollParent(!1)),i.scrollParentNotHidden[0]!==i.document[0]&&"HTML"!==i.scrollParentNotHidden[0].tagName&&(i.overflowOffset=i.scrollParentNotHidden.offset())},drag:function(t,e,i){var s=i.options,n=!1,o=i.scrollParentNotHidden[0],a=i.document[0];o!==a&&"HTML"!==o.tagName?(s.axis&&"x"===s.axis||(i.overflowOffset.top+o.offsetHeight-t.pageY<s.scrollSensitivity?o.scrollTop=n=o.scrollTop+s.scrollSpeed:t.pageY-i.overflowOffset.top<s.scrollSensitivity&&(o.scrollTop=n=o.scrollTop-s.scrollSpeed)),s.axis&&"y"===s.axis||(i.overflowOffset.left+o.offsetWidth-t.pageX<s.scrollSensitivity?o.scrollLeft=n=o.scrollLeft+s.scrollSpeed:t.pageX-i.overflowOffset.left<s.scrollSensitivity&&(o.scrollLeft=n=o.scrollLeft-s.scrollSpeed))):(s.axis&&"x"===s.axis||(t.pageY-V(a).scrollTop()<s.scrollSensitivity?n=V(a).scrollTop(V(a).scrollTop()-s.scrollSpeed):V(window).height()-(t.pageY-V(a).scrollTop())<s.scrollSensitivity&&(n=V(a).scrollTop(V(a).scrollTop()+s.scrollSpeed))),s.axis&&"y"===s.axis||(t.pageX-V(a).scrollLeft()<s.scrollSensitivity?n=V(a).scrollLeft(V(a).scrollLeft()-s.scrollSpeed):V(window).width()-(t.pageX-V(a).scrollLeft())<s.scrollSensitivity&&(n=V(a).scrollLeft(V(a).scrollLeft()+s.scrollSpeed)))),!1!==n&&V.ui.ddmanager&&!s.dropBehaviour&&V.ui.ddmanager.prepareOffsets(i,t)}}),V.ui.plugin.add("draggable","snap",{start:function(t,e,i){var s=i.options;i.snapElements=[],V(s.snap.constructor!==String?s.snap.items||":data(ui-draggable)":s.snap).each(function(){var t=V(this),e=t.offset();this!==i.element[0]&&i.snapElements.push({item:this,width:t.outerWidth(),height:t.outerHeight(),top:e.top,left:e.left})})},drag:function(t,e,i){for(var 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s,i=i.options,i=V.makeArray(V(i.stack)).sort(function(t,e){return(parseInt(V(t).css("zIndex"),10)||0)-(parseInt(V(e).css("zIndex"),10)||0)});i.length&&(s=parseInt(V(i[0]).css("zIndex"),10)||0,V(i).each(function(t){V(this).css("zIndex",s+t)}),this.css("zIndex",s+i.length))}}),V.ui.plugin.add("draggable","zIndex",{start:function(t,e,i){e=V(e.helper),i=i.options;e.css("zIndex")&&(i._zIndex=e.css("zIndex")),e.css("zIndex",i.zIndex)},stop:function(t,e,i){i=i.options;i._zIndex&&V(e.helper).css("zIndex",i._zIndex)}});V.ui.draggable;V.widget("ui.resizable",V.ui.mouse,{version:"1.13.2",widgetEventPrefix:"resize",options:{alsoResize:!1,animate:!1,animateDuration:"slow",animateEasing:"swing",aspectRatio:!1,autoHide:!1,classes:{"ui-resizable-se":"ui-icon ui-icon-gripsmall-diagonal-se"},containment:!1,ghost:!1,grid:!1,handles:"e,s,se",helper:!1,maxHeight:null,maxWidth:null,minHeight:10,minWidth:10,zIndex:90,resize:null,start:null,stop:null},_num:function(t){return parseFloat(t)||0},_isNumber:function(t){return!isNaN(parseFloat(t))},_hasScroll:function(t,e){if("hidden"===V(t).css("overflow"))return!1;var i=e&&"left"===e?"scrollLeft":"scrollTop",e=!1;if(0<t[i])return!0;try{t[i]=1,e=0<t[i],t[i]=0}catch(t){}return e},_create:function(){var t,e=this.options,i=this;this._addClass("ui-resizable"),V.extend(this,{_aspectRatio:!!e.aspectRatio,aspectRatio:e.aspectRatio,originalElement:this.element,_proportionallyResizeElements:[],_helper:e.helper||e.ghost||e.animate?e.helper||"ui-resizable-helper":null}),this.element[0].nodeName.match(/^(canvas|textarea|input|select|button|img)$/i)&&(this.element.wrap(V("<div 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t(t){V(t).removeData("resizable").removeData("ui-resizable").off(".resizable")}var e;return this.elementIsWrapper&&(t(this.element),e=this.element,this.originalElement.css({position:e.css("position"),width:e.outerWidth(),height:e.outerHeight(),top:e.css("top"),left:e.css("left")}).insertAfter(e),e.remove()),this.originalElement.css("resize",this.originalResizeStyle),t(this.originalElement),this},_setOption:function(t,e){switch(this._super(t,e),t){case"handles":this._removeHandles(),this._setupHandles();break;case"aspectRatio":this._aspectRatio=!!e}},_setupHandles:function(){var t,e,i,s,n,o=this.options,a=this;if(this.handles=o.handles||(V(".ui-resizable-handle",this.element).length?{n:".ui-resizable-n",e:".ui-resizable-e",s:".ui-resizable-s",w:".ui-resizable-w",se:".ui-resizable-se",sw:".ui-resizable-sw",ne:".ui-resizable-ne",nw:".ui-resizable-nw"}:"e,s,se"),this._handles=V(),this._addedHandles=V(),this.handles.constructor===String)for("all"===this.handles&&(this.handles="n,e,s,w,se,sw,ne,nw"),i=this.handles.split(","),this.handles={},e=0;e<i.length;e++)s="ui-resizable-"+(t=String.prototype.trim.call(i[e])),n=V("<div>"),this._addClass(n,"ui-resizable-handle "+s),n.css({zIndex:o.zIndex}),this.handles[t]=".ui-resizable-"+t,this.element.children(this.handles[t]).length||(this.element.append(n),this._addedHandles=this._addedHandles.add(n));this._renderAxis=function(t){var e,i,s;for(e in 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s=this.originalSize;return{top:this.originalPosition.top+i,height:s.height-i}},s:function(t,e,i){return{height:this.originalSize.height+i}},se:function(t,e,i){return V.extend(this._change.s.apply(this,arguments),this._change.e.apply(this,[t,e,i]))},sw:function(t,e,i){return V.extend(this._change.s.apply(this,arguments),this._change.w.apply(this,[t,e,i]))},ne:function(t,e,i){return V.extend(this._change.n.apply(this,arguments),this._change.e.apply(this,[t,e,i]))},nw:function(t,e,i){return V.extend(this._change.n.apply(this,arguments),this._change.w.apply(this,[t,e,i]))}},_propagate:function(t,e){V.ui.plugin.call(this,t,[e,this.ui()]),"resize"!==t&&this._trigger(t,e,this.ui())},plugins:{},ui:function(){return{originalElement:this.originalElement,element:this.element,helper:this.helper,position:this.position,size:this.size,originalSize:this.originalSize,originalPosition:this.originalPosition}}}),V.ui.plugin.add("resizable","animate",{stop:function(e){var i=V(this).resizable("instance"),t=i.options,s=i._proportionallyResizeElements,n=s.length&&/textarea/i.test(s[0].nodeName),o=n&&i._hasScroll(s[0],"left")?0:i.sizeDiff.height,a=n?0:i.sizeDiff.width,n={width:i.size.width-a,height:i.size.height-o},a=parseFloat(i.element.css("left"))+(i.position.left-i.originalPosition.left)||null,o=parseFloat(i.element.css("top"))+(i.position.top-i.originalPosition.top)||null;i.element.animate(V.extend(n,o&&a?{top:o,left:a}:{}),{duration:t.animateDuration,easing:t.animateEasing,step:function(){var t={width:parseFloat(i.element.css("width")),height:parseFloat(i.element.css("height")),top:parseFloat(i.element.css("top")),left:parseFloat(i.element.css("left"))};s&&s.length&&V(s[0]).css({width:t.width,height:t.height}),i._updateCache(t),i._propagate("resize",e)}})}}),V.ui.plugin.add("resizable","containment",{start:function(){var i,s,n=V(this).resizable("instance"),t=n.options,e=n.element,o=t.containment,a=o instanceof V?o.get(0):/parent/.test(o)?e.parent().get(0):o;a&&(n.containerElement=V(a),/document/.test(o)||o===document?(n.containerOffset={left:0,top:0},n.containerPosition={left:0,top:0},n.parentData={element:V(document),left:0,top:0,width:V(document).width(),height:V(document).height()||document.body.parentNode.scrollHeight}):(i=V(a),s=[],V(["Top","Right","Left","Bottom"]).each(function(t,e){s[t]=n._num(i.css("padding"+e))}),n.containerOffset=i.offset(),n.containerPosition=i.position(),n.containerSize={height:i.innerHeight()-s[3],width:i.innerWidth()-s[1]},t=n.containerOffset,e=n.containerSize.height,o=n.containerSize.width,o=n._hasScroll(a,"left")?a.scrollWidth:o,e=n._hasScroll(a)?a.scrollHeight:e,n.parentData={element:a,left:t.left,top:t.top,width:o,height:e}))},resize:function(t){var e=V(this).resizable("instance"),i=e.options,s=e.containerOffset,n=e.position,o=e._aspectRatio||t.shiftKey,a={top:0,left:0},r=e.containerElement,t=!0;r[0]!==document&&/static/.test(r.css("position"))&&(a=s),n.left<(e._helper?s.left:0)&&(e.size.width=e.size.width+(e._helper?e.position.left-s.left:e.position.left-a.left),o&&(e.size.height=e.size.width/e.aspectRatio,t=!1),e.position.left=i.helper?s.left:0),n.top<(e._helper?s.top:0)&&(e.size.height=e.size.height+(e._helper?e.position.top-s.top:e.position.top),o&&(e.size.width=e.size.height*e.aspectRatio,t=!1),e.position.top=e._helper?s.top:0),i=e.containerElement.get(0)===e.element.parent().get(0),n=/relative|absolute/.test(e.containerElement.css("position")),i&&n?(e.offset.left=e.parentData.left+e.position.left,e.offset.top=e.parentData.top+e.position.top):(e.offset.left=e.element.offset().left,e.offset.top=e.element.offset().top),n=Math.abs(e.sizeDiff.width+(e._helper?e.offset.left-a.left:e.offset.left-s.left)),s=Math.abs(e.sizeDiff.height+(e._helper?e.offset.top-a.top:e.offset.top-s.top)),n+e.size.width>=e.parentData.width&&(e.size.width=e.parentData.width-n,o&&(e.size.height=e.size.width/e.aspectRatio,t=!1)),s+e.size.height>=e.parentData.height&&(e.size.height=e.parentData.height-s,o&&(e.size.width=e.size.height*e.aspectRatio,t=!1)),t||(e.position.left=e.prevPosition.left,e.position.top=e.prevPosition.top,e.size.width=e.prevSize.width,e.size.height=e.prevSize.height)},stop:function(){var t=V(this).resizable("instance"),e=t.options,i=t.containerOffset,s=t.containerPosition,n=t.containerElement,o=V(t.helper),a=o.offset(),r=o.outerWidth()-t.sizeDiff.width,o=o.outerHeight()-t.sizeDiff.height;t._helper&&!e.animate&&/relative/.test(n.css("position"))&&V(this).css({left:a.left-s.left-i.left,width:r,height:o}),t._helper&&!e.animate&&/static/.test(n.css("position"))&&V(this).css({left:a.left-s.left-i.left,width:r,height:o})}}),V.ui.plugin.add("resizable","alsoResize",{start:function(){var t=V(this).resizable("instance").options;V(t.alsoResize).each(function(){var t=V(this);t.data("ui-resizable-alsoresize",{width:parseFloat(t.width()),height:parseFloat(t.height()),left:parseFloat(t.css("left")),top:parseFloat(t.css("top"))})})},resize:function(t,i){var e=V(this).resizable("instance"),s=e.options,n=e.originalSize,o=e.originalPosition,a={height:e.size.height-n.height||0,width:e.size.width-n.width||0,top:e.position.top-o.top||0,left:e.position.left-o.left||0};V(s.alsoResize).each(function(){var t=V(this),s=V(this).data("ui-resizable-alsoresize"),n={},e=t.parents(i.originalElement[0]).length?["width","height"]:["width","height","top","left"];V.each(e,function(t,e){var i=(s[e]||0)+(a[e]||0);i&&0<=i&&(n[e]=i||null)}),t.css(n)})},stop:function(){V(this).removeData("ui-resizable-alsoresize")}}),V.ui.plugin.add("resizable","ghost",{start:function(){var t=V(this).resizable("instance"),e=t.size;t.ghost=t.originalElement.clone(),t.ghost.css({opacity:.25,display:"block",position:"relative",height:e.height,width:e.width,margin:0,left:0,top:0}),t._addClass(t.ghost,"ui-resizable-ghost"),!1!==V.uiBackCompat&&"string"==typeof t.options.ghost&&t.ghost.addClass(this.options.ghost),t.ghost.appendTo(t.helper)},resize:function(){var t=V(this).resizable("instance");t.ghost&&t.ghost.css({position:"relative",height:t.size.height,width:t.size.width})},stop:function(){var t=V(this).resizable("instance");t.ghost&&t.helper&&t.helper.get(0).removeChild(t.ghost.get(0))}}),V.ui.plugin.add("resizable","grid",{resize:function(){var t,e=V(this).resizable("instance"),i=e.options,s=e.size,n=e.originalSize,o=e.originalPosition,a=e.axis,r="number"==typeof i.grid?[i.grid,i.grid]:i.grid,l=r[0]||1,h=r[1]||1,c=Math.round((s.width-n.width)/l)*l,u=Math.round((s.height-n.height)/h)*h,d=n.width+c,p=n.height+u,f=i.maxWidth&&i.maxWidth<d,g=i.maxHeight&&i.maxHeight<p,m=i.minWidth&&i.minWidth>d,s=i.minHeight&&i.minHeight>p;i.grid=r,m&&(d+=l),s&&(p+=h),f&&(d-=l),g&&(p-=h),/^(se|s|e)$/.test(a)?(e.size.width=d,e.size.height=p):/^(ne)$/.test(a)?(e.size.width=d,e.size.height=p,e.position.top=o.top-u):/^(sw)$/.test(a)?(e.size.width=d,e.size.height=p,e.position.left=o.left-c):((p-h<=0||d-l<=0)&&(t=e._getPaddingPlusBorderDimensions(this)),0<p-h?(e.size.height=p,e.position.top=o.top-u):(p=h-t.height,e.size.height=p,e.position.top=o.top+n.height-p),0<d-l?(e.size.width=d,e.position.left=o.left-c):(d=l-t.width,e.size.width=d,e.position.left=o.left+n.width-d))}});V.ui.resizable;V.widget("ui.dialog",{version:"1.13.2",options:{appendTo:"body",autoOpen:!0,buttons:[],classes:{"ui-dialog":"ui-corner-all","ui-dialog-titlebar":"ui-corner-all"},closeOnEscape:!0,closeText:"Close",draggable:!0,hide:null,height:"auto",maxHeight:null,maxWidth:null,minHeight:150,minWidth:150,modal:!1,position:{my:"center",at:"center",of:window,collision:"fit",using:function(t){var e=V(this).css(t).offset().top;e<0&&V(this).css("top",t.top-e)}},resizable:!0,show:null,title:null,width:300,beforeClose:null,close:null,drag:null,dragStart:null,dragStop:null,focus:null,open:null,resize:null,resizeStart:null,resizeStop:null},sizeRelatedOptions:{buttons:!0,height:!0,maxHeight:!0,maxWidth:!0,minHeight:!0,minWidth:!0,width:!0},resizableRelatedOptions:{maxHeight:!0,maxWidth:!0,minHeight:!0,minWidth:!0},_create:function(){this.originalCss={display:this.element[0].style.display,width:this.element[0].style.width,minHeight:this.element[0].style.minHeight,maxHeight:this.element[0].style.maxHeight,height:this.element[0].style.height},this.originalPosition={parent:this.element.parent(),index:this.element.parent().children().index(this.element)},this.originalTitle=this.element.attr("title"),null==this.options.title&&null!=this.originalTitle&&(this.options.title=this.originalTitle),this.options.disabled&&(this.options.disabled=!1),this._createWrapper(),this.element.show().removeAttr("title").appendTo(this.uiDialog),this._addClass("ui-dialog-content","ui-widget-content"),this._createTitlebar(),this._createButtonPane(),this.options.draggable&&V.fn.draggable&&this._makeDraggable(),this.options.resizable&&V.fn.resizable&&this._makeResizable(),this._isOpen=!1,this._trackFocus()},_init:function(){this.options.autoOpen&&this.open()},_appendTo:function(){var t=this.options.appendTo;return t&&(t.jquery||t.nodeType)?V(t):this.document.find(t||"body").eq(0)},_destroy:function(){var t,e=this.originalPosition;this._untrackInstance(),this._destroyOverlay(),this.element.removeUniqueId().css(this.originalCss).detach(),this.uiDialog.remove(),this.originalTitle&&this.element.attr("title",this.originalTitle),(t=e.parent.children().eq(e.index)).length&&t[0]!==this.element[0]?t.before(this.element):e.parent.append(this.element)},widget:function(){return this.uiDialog},disable:V.noop,enable:V.noop,close:function(t){var e=this;this._isOpen&&!1!==this._trigger("beforeClose",t)&&(this._isOpen=!1,this._focusedElement=null,this._destroyOverlay(),this._untrackInstance(),this.opener.filter(":focusable").trigger("focus").length||V.ui.safeBlur(V.ui.safeActiveElement(this.document[0])),this._hide(this.uiDialog,this.options.hide,function(){e._trigger("close",t)}))},isOpen:function(){return this._isOpen},moveToTop:function(){this._moveToTop()},_moveToTop:function(t,e){var i=!1,s=this.uiDialog.siblings(".ui-front:visible").map(function(){return+V(this).css("z-index")}).get(),s=Math.max.apply(null,s);return s>=+this.uiDialog.css("z-index")&&(this.uiDialog.css("z-index",s+1),i=!0),i&&!e&&this._trigger("focus",t),i},open:function(){var t=this;this._isOpen?this._moveToTop()&&this._focusTabbable():(this._isOpen=!0,this.opener=V(V.ui.safeActiveElement(this.document[0])),this._size(),this._position(),this._createOverlay(),this._moveToTop(null,!0),this.overlay&&this.overlay.css("z-index",this.uiDialog.css("z-index")-1),this._show(this.uiDialog,this.options.show,function(){t._focusTabbable(),t._trigger("focus")}),this._makeFocusTarget(),this._trigger("open"))},_focusTabbable:function(){var t=this._focusedElement;(t=!(t=!(t=!(t=!(t=t||this.element.find("[autofocus]")).length?this.element.find(":tabbable"):t).length?this.uiDialogButtonPane.find(":tabbable"):t).length?this.uiDialogTitlebarClose.filter(":tabbable"):t).length?this.uiDialog:t).eq(0).trigger("focus")},_restoreTabbableFocus:function(){var t=V.ui.safeActiveElement(this.document[0]);this.uiDialog[0]===t||V.contains(this.uiDialog[0],t)||this._focusTabbable()},_keepFocus:function(t){t.preventDefault(),this._restoreTabbableFocus(),this._delay(this._restoreTabbableFocus)},_createWrapper:function(){this.uiDialog=V("<div>").hide().attr({tabIndex:-1,role:"dialog"}).appendTo(this._appendTo()),this._addClass(this.uiDialog,"ui-dialog","ui-widget ui-widget-content ui-front"),this._on(this.uiDialog,{keydown:function(t){if(this.options.closeOnEscape&&!t.isDefaultPrevented()&&t.keyCode&&t.keyCode===V.ui.keyCode.ESCAPE)return t.preventDefault(),void this.close(t);var e,i,s;t.keyCode!==V.ui.keyCode.TAB||t.isDefaultPrevented()||(e=this.uiDialog.find(":tabbable"),i=e.first(),s=e.last(),t.target!==s[0]&&t.target!==this.uiDialog[0]||t.shiftKey?t.target!==i[0]&&t.target!==this.uiDialog[0]||!t.shiftKey||(this._delay(function(){s.trigger("focus")}),t.preventDefault()):(this._delay(function(){i.trigger("focus")}),t.preventDefault()))},mousedown:function(t){this._moveToTop(t)&&this._focusTabbable()}}),this.element.find("[aria-describedby]").length||this.uiDialog.attr({"aria-describedby":this.element.uniqueId().attr("id")})},_createTitlebar:function(){var t;this.uiDialogTitlebar=V("<div>"),this._addClass(this.uiDialogTitlebar,"ui-dialog-titlebar","ui-widget-header ui-helper-clearfix"),this._on(this.uiDialogTitlebar,{mousedown:function(t){V(t.target).closest(".ui-dialog-titlebar-close")||this.uiDialog.trigger("focus")}}),this.uiDialogTitlebarClose=V("<button type='button'></button>").button({label:V("<a>").text(this.options.closeText).html(),icon:"ui-icon-closethick",showLabel:!1}).appendTo(this.uiDialogTitlebar),this._addClass(this.uiDialogTitlebarClose,"ui-dialog-titlebar-close"),this._on(this.uiDialogTitlebarClose,{click:function(t){t.preventDefault(),this.close(t)}}),t=V("<span>").uniqueId().prependTo(this.uiDialogTitlebar),this._addClass(t,"ui-dialog-title"),this._title(t),this.uiDialogTitlebar.prependTo(this.uiDialog),this.uiDialog.attr({"aria-labelledby":t.attr("id")})},_title:function(t){this.options.title?t.text(this.options.title):t.html("&#160;")},_createButtonPane:function(){this.uiDialogButtonPane=V("<div>"),this._addClass(this.uiDialogButtonPane,"ui-dialog-buttonpane","ui-widget-content ui-helper-clearfix"),this.uiButtonSet=V("<div>").appendTo(this.uiDialogButtonPane),this._addClass(this.uiButtonSet,"ui-dialog-buttonset"),this._createButtons()},_createButtons:function(){var s=this,t=this.options.buttons;this.uiDialogButtonPane.remove(),this.uiButtonSet.empty(),V.isEmptyObject(t)||Array.isArray(t)&&!t.length?this._removeClass(this.uiDialog,"ui-dialog-buttons"):(V.each(t,function(t,e){var i;e=V.extend({type:"button"},e="function"==typeof e?{click:e,text:t}:e),i=e.click,t={icon:e.icon,iconPosition:e.iconPosition,showLabel:e.showLabel,icons:e.icons,text:e.text},delete e.click,delete e.icon,delete e.iconPosition,delete e.showLabel,delete e.icons,"boolean"==typeof e.text&&delete e.text,V("<button></button>",e).button(t).appendTo(s.uiButtonSet).on("click",function(){i.apply(s.element[0],arguments)})}),this._addClass(this.uiDialog,"ui-dialog-buttons"),this.uiDialogButtonPane.appendTo(this.uiDialog))},_makeDraggable:function(){var n=this,o=this.options;function a(t){return{position:t.position,offset:t.offset}}this.uiDialog.draggable({cancel:".ui-dialog-content, .ui-dialog-titlebar-close",handle:".ui-dialog-titlebar",containment:"document",start:function(t,e){n._addClass(V(this),"ui-dialog-dragging"),n._blockFrames(),n._trigger("dragStart",t,a(e))},drag:function(t,e){n._trigger("drag",t,a(e))},stop:function(t,e){var i=e.offset.left-n.document.scrollLeft(),s=e.offset.top-n.document.scrollTop();o.position={my:"left top",at:"left"+(0<=i?"+":"")+i+" top"+(0<=s?"+":"")+s,of:n.window},n._removeClass(V(this),"ui-dialog-dragging"),n._unblockFrames(),n._trigger("dragStop",t,a(e))}})},_makeResizable:function(){var n=this,o=this.options,t=o.resizable,e=this.uiDialog.css("position"),t="string"==typeof t?t:"n,e,s,w,se,sw,ne,nw";function a(t){return{originalPosition:t.originalPosition,originalSize:t.originalSize,position:t.position,size:t.size}}this.uiDialog.resizable({cancel:".ui-dialog-content",containment:"document",alsoResize:this.element,maxWidth:o.maxWidth,maxHeight:o.maxHeight,minWidth:o.minWidth,minHeight:this._minHeight(),handles:t,start:function(t,e){n._addClass(V(this),"ui-dialog-resizing"),n._blockFrames(),n._trigger("resizeStart",t,a(e))},resize:function(t,e){n._trigger("resize",t,a(e))},stop:function(t,e){var i=n.uiDialog.offset(),s=i.left-n.document.scrollLeft(),i=i.top-n.document.scrollTop();o.height=n.uiDialog.height(),o.width=n.uiDialog.width(),o.position={my:"left top",at:"left"+(0<=s?"+":"")+s+" top"+(0<=i?"+":"")+i,of:n.window},n._removeClass(V(this),"ui-dialog-resizing"),n._unblockFrames(),n._trigger("resizeStop",t,a(e))}}).css("position",e)},_trackFocus:function(){this._on(this.widget(),{focusin:function(t){this._makeFocusTarget(),this._focusedElement=V(t.target)}})},_makeFocusTarget:function(){this._untrackInstance(),this._trackingInstances().unshift(this)},_untrackInstance:function(){var t=this._trackingInstances(),e=V.inArray(this,t);-1!==e&&t.splice(e,1)},_trackingInstances:function(){var t=this.document.data("ui-dialog-instances");return t||this.document.data("ui-dialog-instances",t=[]),t},_minHeight:function(){var t=this.options;return"auto"===t.height?t.minHeight:Math.min(t.minHeight,t.height)},_position:function(){var t=this.uiDialog.is(":visible");t||this.uiDialog.show(),this.uiDialog.position(this.options.position),t||this.uiDialog.hide()},_setOptions:function(t){var i=this,s=!1,n={};V.each(t,function(t,e){i._setOption(t,e),t in i.sizeRelatedOptions&&(s=!0),t in i.resizableRelatedOptions&&(n[t]=e)}),s&&(this._size(),this._position()),this.uiDialog.is(":data(ui-resizable)")&&this.uiDialog.resizable("option",n)},_setOption:function(t,e){var i,s=this.uiDialog;"disabled"!==t&&(this._super(t,e),"appendTo"===t&&this.uiDialog.appendTo(this._appendTo()),"buttons"===t&&this._createButtons(),"closeText"===t&&this.uiDialogTitlebarClose.button({label:V("<a>").text(""+this.options.closeText).html()}),"draggable"===t&&((i=s.is(":data(ui-draggable)"))&&!e&&s.draggable("destroy"),!i&&e&&this._makeDraggable()),"position"===t&&this._position(),"resizable"===t&&((i=s.is(":data(ui-resizable)"))&&!e&&s.resizable("destroy"),i&&"string"==typeof e&&s.resizable("option","handles",e),i||!1===e||this._makeResizable()),"title"===t&&this._title(this.uiDialogTitlebar.find(".ui-dialog-title")))},_size:function(){var t,e,i,s=this.options;this.element.show().css({width:"auto",minHeight:0,maxHeight:"none",height:0}),s.minWidth>s.width&&(s.width=s.minWidth),t=this.uiDialog.css({height:"auto",width:s.width}).outerHeight(),e=Math.max(0,s.minHeight-t),i="number"==typeof s.maxHeight?Math.max(0,s.maxHeight-t):"none","auto"===s.height?this.element.css({minHeight:e,maxHeight:i,height:"auto"}):this.element.height(Math.max(0,s.height-t)),this.uiDialog.is(":data(ui-resizable)")&&this.uiDialog.resizable("option","minHeight",this._minHeight())},_blockFrames:function(){this.iframeBlocks=this.document.find("iframe").map(function(){var t=V(this);return V("<div>").css({position:"absolute",width:t.outerWidth(),height:t.outerHeight()}).appendTo(t.parent()).offset(t.offset())[0]})},_unblockFrames:function(){this.iframeBlocks&&(this.iframeBlocks.remove(),delete this.iframeBlocks)},_allowInteraction:function(t){return!!V(t.target).closest(".ui-dialog").length||!!V(t.target).closest(".ui-datepicker").length},_createOverlay:function(){var i,s;this.options.modal&&(i=V.fn.jquery.substring(0,4),s=!0,this._delay(function(){s=!1}),this.document.data("ui-dialog-overlays")||this.document.on("focusin.ui-dialog",function(t){var e;s||((e=this._trackingInstances()[0])._allowInteraction(t)||(t.preventDefault(),e._focusTabbable(),"3.4."!==i&&"3.5."!==i||e._delay(e._restoreTabbableFocus)))}.bind(this)),this.overlay=V("<div>").appendTo(this._appendTo()),this._addClass(this.overlay,null,"ui-widget-overlay ui-front"),this._on(this.overlay,{mousedown:"_keepFocus"}),this.document.data("ui-dialog-overlays",(this.document.data("ui-dialog-overlays")||0)+1))},_destroyOverlay:function(){var t;this.options.modal&&this.overlay&&((t=this.document.data("ui-dialog-overlays")-1)?this.document.data("ui-dialog-overlays",t):(this.document.off("focusin.ui-dialog"),this.document.removeData("ui-dialog-overlays")),this.overlay.remove(),this.overlay=null)}}),!1!==V.uiBackCompat&&V.widget("ui.dialog",V.ui.dialog,{options:{dialogClass:""},_createWrapper:function(){this._super(),this.uiDialog.addClass(this.options.dialogClass)},_setOption:function(t,e){"dialogClass"===t&&this.uiDialog.removeClass(this.options.dialogClass).addClass(e),this._superApply(arguments)}});V.ui.dialog;function lt(t,e,i){return e<=t&&t<e+i}V.widget("ui.droppable",{version:"1.13.2",widgetEventPrefix:"drop",options:{accept:"*",addClasses:!0,greedy:!1,scope:"default",tolerance:"intersect",activate:null,deactivate:null,drop:null,out:null,over:null},_create:function(){var t,e=this.options,i=e.accept;this.isover=!1,this.isout=!0,this.accept="function"==typeof i?i:function(t){return t.is(i)},this.proportions=function(){if(!arguments.length)return t=t||{width:this.element[0].offsetWidth,height:this.element[0].offsetHeight};t=arguments[0]},this._addToManager(e.scope),e.addClasses&&this._addClass("ui-droppable")},_addToManager:function(t){V.ui.ddmanager.droppables[t]=V.ui.ddmanager.droppables[t]||[],V.ui.ddmanager.droppables[t].push(this)},_splice:function(t){for(var e=0;e<t.length;e++)t[e]===this&&t.splice(e,1)},_destroy:function(){var t=V.ui.ddmanager.droppables[this.options.scope];this._splice(t)},_setOption:function(t,e){var i;"accept"===t?this.accept="function"==typeof e?e:function(t){return t.is(e)}:"scope"===t&&(i=V.ui.ddmanager.droppables[this.options.scope],this._splice(i),this._addToManager(e)),this._super(t,e)},_activate:function(t){var e=V.ui.ddmanager.current;this._addActiveClass(),e&&this._trigger("activate",t,this.ui(e))},_deactivate:function(t){var e=V.ui.ddmanager.current;this._removeActiveClass(),e&&this._trigger("deactivate",t,this.ui(e))},_over:function(t){var e=V.ui.ddmanager.current;e&&(e.currentItem||e.element)[0]!==this.element[0]&&this.accept.call(this.element[0],e.currentItem||e.element)&&(this._addHoverClass(),this._trigger("over",t,this.ui(e)))},_out:function(t){var e=V.ui.ddmanager.current;e&&(e.currentItem||e.element)[0]!==this.element[0]&&this.accept.call(this.element[0],e.currentItem||e.element)&&(this._removeHoverClass(),this._trigger("out",t,this.ui(e)))},_drop:function(e,t){var i=t||V.ui.ddmanager.current,s=!1;return!(!i||(i.currentItem||i.element)[0]===this.element[0])&&(this.element.find(":data(ui-droppable)").not(".ui-draggable-dragging").each(function(){var t=V(this).droppable("instance");if(t.options.greedy&&!t.options.disabled&&t.options.scope===i.options.scope&&t.accept.call(t.element[0],i.currentItem||i.element)&&V.ui.intersect(i,V.extend(t,{offset:t.element.offset()}),t.options.tolerance,e))return!(s=!0)}),!s&&(!!this.accept.call(this.element[0],i.currentItem||i.element)&&(this._removeActiveClass(),this._removeHoverClass(),this._trigger("drop",e,this.ui(i)),this.element)))},ui:function(t){return{draggable:t.currentItem||t.element,helper:t.helper,position:t.position,offset:t.positionAbs}},_addHoverClass:function(){this._addClass("ui-droppable-hover")},_removeHoverClass:function(){this._removeClass("ui-droppable-hover")},_addActiveClass:function(){this._addClass("ui-droppable-active")},_removeActiveClass:function(){this._removeClass("ui-droppable-active")}}),V.ui.intersect=function(t,e,i,s){if(!e.offset)return!1;var n=(t.positionAbs||t.position.absolute).left+t.margins.left,o=(t.positionAbs||t.position.absolute).top+t.margins.top,a=n+t.helperProportions.width,r=o+t.helperProportions.height,l=e.offset.left,h=e.offset.top,c=l+e.proportions().width,u=h+e.proportions().height;switch(i){case"fit":return l<=n&&a<=c&&h<=o&&r<=u;case"intersect":return l<n+t.helperProportions.width/2&&a-t.helperProportions.width/2<c&&h<o+t.helperProportions.height/2&&r-t.helperProportions.height/2<u;case"pointer":return lt(s.pageY,h,e.proportions().height)&&lt(s.pageX,l,e.proportions().width);case"touch":return(h<=o&&o<=u||h<=r&&r<=u||o<h&&u<r)&&(l<=n&&n<=c||l<=a&&a<=c||n<l&&c<a);default:return!1}},!(V.ui.ddmanager={current:null,droppables:{default:[]},prepareOffsets:function(t,e){var i,s,n=V.ui.ddmanager.droppables[t.options.scope]||[],o=e?e.type:null,a=(t.currentItem||t.element).find(":data(ui-droppable)").addBack();t:for(i=0;i<n.length;i++)if(!(n[i].options.disabled||t&&!n[i].accept.call(n[i].element[0],t.currentItem||t.element))){for(s=0;s<a.length;s++)if(a[s]===n[i].element[0]){n[i].proportions().height=0;continue t}n[i].visible="none"!==n[i].element.css("display"),n[i].visible&&("mousedown"===o&&n[i]._activate.call(n[i],e),n[i].offset=n[i].element.offset(),n[i].proportions({width:n[i].element[0].offsetWidth,height:n[i].element[0].offsetHeight}))}},drop:function(t,e){var i=!1;return V.each((V.ui.ddmanager.droppables[t.options.scope]||[]).slice(),function(){this.options&&(!this.options.disabled&&this.visible&&V.ui.intersect(t,this,this.options.tolerance,e)&&(i=this._drop.call(this,e)||i),!this.options.disabled&&this.visible&&this.accept.call(this.element[0],t.currentItem||t.element)&&(this.isout=!0,this.isover=!1,this._deactivate.call(this,e)))}),i},dragStart:function(t,e){t.element.parentsUntil("body").on("scroll.droppable",function(){t.options.refreshPositions||V.ui.ddmanager.prepareOffsets(t,e)})},drag:function(n,o){n.options.refreshPositions&&V.ui.ddmanager.prepareOffsets(n,o),V.each(V.ui.ddmanager.droppables[n.options.scope]||[],function(){var t,e,i,s;this.options.disabled||this.greedyChild||!this.visible||(s=!(i=V.ui.intersect(n,this,this.options.tolerance,o))&&this.isover?"isout":i&&!this.isover?"isover":null)&&(this.options.greedy&&(e=this.options.scope,(i=this.element.parents(":data(ui-droppable)").filter(function(){return V(this).droppable("instance").options.scope===e})).length&&((t=V(i[0]).droppable("instance")).greedyChild="isover"===s)),t&&"isover"===s&&(t.isover=!1,t.isout=!0,t._out.call(t,o)),this[s]=!0,this["isout"===s?"isover":"isout"]=!1,this["isover"===s?"_over":"_out"].call(this,o),t&&"isout"===s&&(t.isout=!1,t.isover=!0,t._over.call(t,o)))})},dragStop:function(t,e){t.element.parentsUntil("body").off("scroll.droppable"),t.options.refreshPositions||V.ui.ddmanager.prepareOffsets(t,e)}})!==V.uiBackCompat&&V.widget("ui.droppable",V.ui.droppable,{options:{hoverClass:!1,activeClass:!1},_addActiveClass:function(){this._super(),this.options.activeClass&&this.element.addClass(this.options.activeClass)},_removeActiveClass:function(){this._super(),this.options.activeClass&&this.element.removeClass(this.options.activeClass)},_addHoverClass:function(){this._super(),this.options.hoverClass&&this.element.addClass(this.options.hoverClass)},_removeHoverClass:function(){this._super(),this.options.hoverClass&&this.element.removeClass(this.options.hoverClass)}});V.ui.droppable,V.widget("ui.progressbar",{version:"1.13.2",options:{classes:{"ui-progressbar":"ui-corner-all","ui-progressbar-value":"ui-corner-left","ui-progressbar-complete":"ui-corner-right"},max:100,value:0,change:null,complete:null},min:0,_create:function(){this.oldValue=this.options.value=this._constrainedValue(),this.element.attr({role:"progressbar","aria-valuemin":this.min}),this._addClass("ui-progressbar","ui-widget ui-widget-content"),this.valueDiv=V("<div>").appendTo(this.element),this._addClass(this.valueDiv,"ui-progressbar-value","ui-widget-header"),this._refreshValue()},_destroy:function(){this.element.removeAttr("role aria-valuemin aria-valuemax aria-valuenow"),this.valueDiv.remove()},value:function(t){if(void 0===t)return this.options.value;this.options.value=this._constrainedValue(t),this._refreshValue()},_constrainedValue:function(t){return void 0===t&&(t=this.options.value),this.indeterminate=!1===t,"number"!=typeof t&&(t=0),!this.indeterminate&&Math.min(this.options.max,Math.max(this.min,t))},_setOptions:function(t){var e=t.value;delete t.value,this._super(t),this.options.value=this._constrainedValue(e),this._refreshValue()},_setOption:function(t,e){"max"===t&&(e=Math.max(this.min,e)),this._super(t,e)},_setOptionDisabled:function(t){this._super(t),this.element.attr("aria-disabled",t),this._toggleClass(null,"ui-state-disabled",!!t)},_percentage:function(){return this.indeterminate?100:100*(this.options.value-this.min)/(this.options.max-this.min)},_refreshValue:function(){var t=this.options.value,e=this._percentage();this.valueDiv.toggle(this.indeterminate||t>this.min).width(e.toFixed(0)+"%"),this._toggleClass(this.valueDiv,"ui-progressbar-complete",null,t===this.options.max)._toggleClass("ui-progressbar-indeterminate",null,this.indeterminate),this.indeterminate?(this.element.removeAttr("aria-valuenow"),this.overlayDiv||(this.overlayDiv=V("<div>").appendTo(this.valueDiv),this._addClass(this.overlayDiv,"ui-progressbar-overlay"))):(this.element.attr({"aria-valuemax":this.options.max,"aria-valuenow":t}),this.overlayDiv&&(this.overlayDiv.remove(),this.overlayDiv=null)),this.oldValue!==t&&(this.oldValue=t,this._trigger("change")),t===this.options.max&&this._trigger("complete")}}),V.widget("ui.selectable",V.ui.mouse,{version:"1.13.2",options:{appendTo:"body",autoRefresh:!0,distance:0,filter:"*",tolerance:"touch",selected:null,selecting:null,start:null,stop:null,unselected:null,unselecting:null},_create:function(){var i=this;this._addClass("ui-selectable"),this.dragged=!1,this.refresh=function(){i.elementPos=V(i.element[0]).offset(),i.selectees=V(i.options.filter,i.element[0]),i._addClass(i.selectees,"ui-selectee"),i.selectees.each(function(){var t=V(this),e=t.offset(),e={left:e.left-i.elementPos.left,top:e.top-i.elementPos.top};V.data(this,"selectable-item",{element:this,$element:t,left:e.left,top:e.top,right:e.left+t.outerWidth(),bottom:e.top+t.outerHeight(),startselected:!1,selected:t.hasClass("ui-selected"),selecting:t.hasClass("ui-selecting"),unselecting:t.hasClass("ui-unselecting")})})},this.refresh(),this._mouseInit(),this.helper=V("<div>"),this._addClass(this.helper,"ui-selectable-helper")},_destroy:function(){this.selectees.removeData("selectable-item"),this._mouseDestroy()},_mouseStart:function(i){var s=this,t=this.options;this.opos=[i.pageX,i.pageY],this.elementPos=V(this.element[0]).offset(),this.options.disabled||(this.selectees=V(t.filter,this.element[0]),this._trigger("start",i),V(t.appendTo).append(this.helper),this.helper.css({left:i.pageX,top:i.pageY,width:0,height:0}),t.autoRefresh&&this.refresh(),this.selectees.filter(".ui-selected").each(function(){var t=V.data(this,"selectable-item");t.startselected=!0,i.metaKey||i.ctrlKey||(s._removeClass(t.$element,"ui-selected"),t.selected=!1,s._addClass(t.$element,"ui-unselecting"),t.unselecting=!0,s._trigger("unselecting",i,{unselecting:t.element}))}),V(i.target).parents().addBack().each(function(){var t,e=V.data(this,"selectable-item");if(e)return t=!i.metaKey&&!i.ctrlKey||!e.$element.hasClass("ui-selected"),s._removeClass(e.$element,t?"ui-unselecting":"ui-selected")._addClass(e.$element,t?"ui-selecting":"ui-unselecting"),e.unselecting=!t,e.selecting=t,(e.selected=t)?s._trigger("selecting",i,{selecting:e.element}):s._trigger("unselecting",i,{unselecting:e.element}),!1}))},_mouseDrag:function(s){if(this.dragged=!0,!this.options.disabled){var t,n=this,o=this.options,a=this.opos[0],r=this.opos[1],l=s.pageX,h=s.pageY;return l<a&&(t=l,l=a,a=t),h<r&&(t=h,h=r,r=t),this.helper.css({left:a,top:r,width:l-a,height:h-r}),this.selectees.each(function(){var t=V.data(this,"selectable-item"),e=!1,i={};t&&t.element!==n.element[0]&&(i.left=t.left+n.elementPos.left,i.right=t.right+n.elementPos.left,i.top=t.top+n.elementPos.top,i.bottom=t.bottom+n.elementPos.top,"touch"===o.tolerance?e=!(i.left>l||i.right<a||i.top>h||i.bottom<r):"fit"===o.tolerance&&(e=i.left>a&&i.right<l&&i.top>r&&i.bottom<h),e?(t.selected&&(n._removeClass(t.$element,"ui-selected"),t.selected=!1),t.unselecting&&(n._removeClass(t.$element,"ui-unselecting"),t.unselecting=!1),t.selecting||(n._addClass(t.$element,"ui-selecting"),t.selecting=!0,n._trigger("selecting",s,{selecting:t.element}))):(t.selecting&&((s.metaKey||s.ctrlKey)&&t.startselected?(n._removeClass(t.$element,"ui-selecting"),t.selecting=!1,n._addClass(t.$element,"ui-selected"),t.selected=!0):(n._removeClass(t.$element,"ui-selecting"),t.selecting=!1,t.startselected&&(n._addClass(t.$element,"ui-unselecting"),t.unselecting=!0),n._trigger("unselecting",s,{unselecting:t.element}))),t.selected&&(s.metaKey||s.ctrlKey||t.startselected||(n._removeClass(t.$element,"ui-selected"),t.selected=!1,n._addClass(t.$element,"ui-unselecting"),t.unselecting=!0,n._trigger("unselecting",s,{unselecting:t.element})))))}),!1}},_mouseStop:function(e){var i=this;return this.dragged=!1,V(".ui-unselecting",this.element[0]).each(function(){var t=V.data(this,"selectable-item");i._removeClass(t.$element,"ui-unselecting"),t.unselecting=!1,t.startselected=!1,i._trigger("unselected",e,{unselected:t.element})}),V(".ui-selecting",this.element[0]).each(function(){var t=V.data(this,"selectable-item");i._removeClass(t.$element,"ui-selecting")._addClass(t.$element,"ui-selected"),t.selecting=!1,t.selected=!0,t.startselected=!0,i._trigger("selected",e,{selected:t.element})}),this._trigger("stop",e),this.helper.remove(),!1}}),V.widget("ui.selectmenu",[V.ui.formResetMixin,{version:"1.13.2",defaultElement:"<select>",options:{appendTo:null,classes:{"ui-selectmenu-button-open":"ui-corner-top","ui-selectmenu-button-closed":"ui-corner-all"},disabled:null,icons:{button:"ui-icon-triangle-1-s"},position:{my:"left top",at:"left bottom",collision:"none"},width:!1,change:null,close:null,focus:null,open:null,select:null},_create:function(){var t=this.element.uniqueId().attr("id");this.ids={element:t,button:t+"-button",menu:t+"-menu"},this._drawButton(),this._drawMenu(),this._bindFormResetHandler(),this._rendered=!1,this.menuItems=V()},_drawButton:function(){var t,e=this,i=this._parseOption(this.element.find("option:selected"),this.element[0].selectedIndex);this.labels=this.element.labels().attr("for",this.ids.button),this._on(this.labels,{click:function(t){this.button.trigger("focus"),t.preventDefault()}}),this.element.hide(),this.button=V("<span>",{tabindex:this.options.disabled?-1:0,id:this.ids.button,role:"combobox","aria-expanded":"false","aria-autocomplete":"list","aria-owns":this.ids.menu,"aria-haspopup":"true",title:this.element.attr("title")}).insertAfter(this.element),this._addClass(this.button,"ui-selectmenu-button ui-selectmenu-button-closed","ui-button ui-widget"),t=V("<span>").appendTo(this.button),this._addClass(t,"ui-selectmenu-icon","ui-icon "+this.options.icons.button),this.buttonItem=this._renderButtonItem(i).appendTo(this.button),!1!==this.options.width&&this._resizeButton(),this._on(this.button,this._buttonEvents),this.button.one("focusin",function(){e._rendered||e._refreshMenu()})},_drawMenu:function(){var i=this;this.menu=V("<ul>",{"aria-hidden":"true","aria-labelledby":this.ids.button,id:this.ids.menu}),this.menuWrap=V("<div>").append(this.menu),this._addClass(this.menuWrap,"ui-selectmenu-menu","ui-front"),this.menuWrap.appendTo(this._appendTo()),this.menuInstance=this.menu.menu({classes:{"ui-menu":"ui-corner-bottom"},role:"listbox",select:function(t,e){t.preventDefault(),i._setSelection(),i._select(e.item.data("ui-selectmenu-item"),t)},focus:function(t,e){e=e.item.data("ui-selectmenu-item");null!=i.focusIndex&&e.index!==i.focusIndex&&(i._trigger("focus",t,{item:e}),i.isOpen||i._select(e,t)),i.focusIndex=e.index,i.button.attr("aria-activedescendant",i.menuItems.eq(e.index).attr("id"))}}).menu("instance"),this.menuInstance._off(this.menu,"mouseleave"),this.menuInstance._closeOnDocumentClick=function(){return!1},this.menuInstance._isDivider=function(){return!1}},refresh:function(){this._refreshMenu(),this.buttonItem.replaceWith(this.buttonItem=this._renderButtonItem(this._getSelectedItem().data("ui-selectmenu-item")||{})),null===this.options.width&&this._resizeButton()},_refreshMenu:function(){var t=this.element.find("option");this.menu.empty(),this._parseOptions(t),this._renderMenu(this.menu,this.items),this.menuInstance.refresh(),this.menuItems=this.menu.find("li").not(".ui-selectmenu-optgroup").find(".ui-menu-item-wrapper"),this._rendered=!0,t.length&&(t=this._getSelectedItem(),this.menuInstance.focus(null,t),this._setAria(t.data("ui-selectmenu-item")),this._setOption("disabled",this.element.prop("disabled")))},open:function(t){this.options.disabled||(this._rendered?(this._removeClass(this.menu.find(".ui-state-active"),null,"ui-state-active"),this.menuInstance.focus(null,this._getSelectedItem())):this._refreshMenu(),this.menuItems.length&&(this.isOpen=!0,this._toggleAttr(),this._resizeMenu(),this._position(),this._on(this.document,this._documentClick),this._trigger("open",t)))},_position:function(){this.menuWrap.position(V.extend({of:this.button},this.options.position))},close:function(t){this.isOpen&&(this.isOpen=!1,this._toggleAttr(),this.range=null,this._off(this.document),this._trigger("close",t))},widget:function(){return this.button},menuWidget:function(){return this.menu},_renderButtonItem:function(t){var e=V("<span>");return this._setText(e,t.label),this._addClass(e,"ui-selectmenu-text"),e},_renderMenu:function(s,t){var n=this,o="";V.each(t,function(t,e){var i;e.optgroup!==o&&(i=V("<li>",{text:e.optgroup}),n._addClass(i,"ui-selectmenu-optgroup","ui-menu-divider"+(e.element.parent("optgroup").prop("disabled")?" ui-state-disabled":"")),i.appendTo(s),o=e.optgroup),n._renderItemData(s,e)})},_renderItemData:function(t,e){return this._renderItem(t,e).data("ui-selectmenu-item",e)},_renderItem:function(t,e){var i=V("<li>"),s=V("<div>",{title:e.element.attr("title")});return e.disabled&&this._addClass(i,null,"ui-state-disabled"),this._setText(s,e.label),i.append(s).appendTo(t)},_setText:function(t,e){e?t.text(e):t.html("&#160;")},_move:function(t,e){var i,s=".ui-menu-item";this.isOpen?i=this.menuItems.eq(this.focusIndex).parent("li"):(i=this.menuItems.eq(this.element[0].selectedIndex).parent("li"),s+=":not(.ui-state-disabled)"),(s="first"===t||"last"===t?i["first"===t?"prevAll":"nextAll"](s).eq(-1):i[t+"All"](s).eq(0)).length&&this.menuInstance.focus(e,s)},_getSelectedItem:function(){return this.menuItems.eq(this.element[0].selectedIndex).parent("li")},_toggle:function(t){this[this.isOpen?"close":"open"](t)},_setSelection:function(){var t;this.range&&(window.getSelection?((t=window.getSelection()).removeAllRanges(),t.addRange(this.range)):this.range.select(),this.button.trigger("focus"))},_documentClick:{mousedown:function(t){this.isOpen&&(V(t.target).closest(".ui-selectmenu-menu, #"+V.escapeSelector(this.ids.button)).length||this.close(t))}},_buttonEvents:{mousedown:function(){var t;window.getSelection?(t=window.getSelection()).rangeCount&&(this.range=t.getRangeAt(0)):this.range=document.selection.createRange()},click:function(t){this._setSelection(),this._toggle(t)},keydown:function(t){var e=!0;switch(t.keyCode){case V.ui.keyCode.TAB:case V.ui.keyCode.ESCAPE:this.close(t),e=!1;break;case V.ui.keyCode.ENTER:this.isOpen&&this._selectFocusedItem(t);break;case V.ui.keyCode.UP:t.altKey?this._toggle(t):this._move("prev",t);break;case V.ui.keyCode.DOWN:t.altKey?this._toggle(t):this._move("next",t);break;case V.ui.keyCode.SPACE:this.isOpen?this._selectFocusedItem(t):this._toggle(t);break;case V.ui.keyCode.LEFT:this._move("prev",t);break;case V.ui.keyCode.RIGHT:this._move("next",t);break;case V.ui.keyCode.HOME:case V.ui.keyCode.PAGE_UP:this._move("first",t);break;case V.ui.keyCode.END:case V.ui.keyCode.PAGE_DOWN:this._move("last",t);break;default:this.menu.trigger(t),e=!1}e&&t.preventDefault()}},_selectFocusedItem:function(t){var e=this.menuItems.eq(this.focusIndex).parent("li");e.hasClass("ui-state-disabled")||this._select(e.data("ui-selectmenu-item"),t)},_select:function(t,e){var i=this.element[0].selectedIndex;this.element[0].selectedIndex=t.index,this.buttonItem.replaceWith(this.buttonItem=this._renderButtonItem(t)),this._setAria(t),this._trigger("select",e,{item:t}),t.index!==i&&this._trigger("change",e,{item:t}),this.close(e)},_setAria:function(t){t=this.menuItems.eq(t.index).attr("id");this.button.attr({"aria-labelledby":t,"aria-activedescendant":t}),this.menu.attr("aria-activedescendant",t)},_setOption:function(t,e){var i;"icons"===t&&(i=this.button.find("span.ui-icon"),this._removeClass(i,null,this.options.icons.button)._addClass(i,null,e.button)),this._super(t,e),"appendTo"===t&&this.menuWrap.appendTo(this._appendTo()),"width"===t&&this._resizeButton()},_setOptionDisabled:function(t){this._super(t),this.menuInstance.option("disabled",t),this.button.attr("aria-disabled",t),this._toggleClass(this.button,null,"ui-state-disabled",t),this.element.prop("disabled",t),t?(this.button.attr("tabindex",-1),this.close()):this.button.attr("tabindex",0)},_appendTo:function(){var t=this.options.appendTo;return t=!(t=!(t=t&&(t.jquery||t.nodeType?V(t):this.document.find(t).eq(0)))||!t[0]?this.element.closest(".ui-front, dialog"):t).length?this.document[0].body:t},_toggleAttr:function(){this.button.attr("aria-expanded",this.isOpen),this._removeClass(this.button,"ui-selectmenu-button-"+(this.isOpen?"closed":"open"))._addClass(this.button,"ui-selectmenu-button-"+(this.isOpen?"open":"closed"))._toggleClass(this.menuWrap,"ui-selectmenu-open",null,this.isOpen),this.menu.attr("aria-hidden",!this.isOpen)},_resizeButton:function(){var t=this.options.width;!1!==t?(null===t&&(t=this.element.show().outerWidth(),this.element.hide()),this.button.outerWidth(t)):this.button.css("width","")},_resizeMenu:function(){this.menu.outerWidth(Math.max(this.button.outerWidth(),this.menu.width("").outerWidth()+1))},_getCreateOptions:function(){var t=this._super();return t.disabled=this.element.prop("disabled"),t},_parseOptions:function(t){var i=this,s=[];t.each(function(t,e){e.hidden||s.push(i._parseOption(V(e),t))}),this.items=s},_parseOption:function(t,e){var i=t.parent("optgroup");return{element:t,index:e,value:t.val(),label:t.text(),optgroup:i.attr("label")||"",disabled:i.prop("disabled")||t.prop("disabled")}},_destroy:function(){this._unbindFormResetHandler(),this.menuWrap.remove(),this.button.remove(),this.element.show(),this.element.removeUniqueId(),this.labels.attr("for",this.ids.element)}}]),V.widget("ui.slider",V.ui.mouse,{version:"1.13.2",widgetEventPrefix:"slide",options:{animate:!1,classes:{"ui-slider":"ui-corner-all","ui-slider-handle":"ui-corner-all","ui-slider-range":"ui-corner-all ui-widget-header"},distance:0,max:100,min:0,orientation:"horizontal",range:!1,step:1,value:0,values:null,change:null,slide:null,start:null,stop:null},numPages:5,_create:function(){this._keySliding=!1,this._mouseSliding=!1,this._animateOff=!0,this._handleIndex=null,this._detectOrientation(),this._mouseInit(),this._calculateNewMax(),this._addClass("ui-slider ui-slider-"+this.orientation,"ui-widget ui-widget-content"),this._refresh(),this._animateOff=!1},_refresh:function(){this._createRange(),this._createHandles(),this._setupEvents(),this._refreshValue()},_createHandles:function(){var t,e=this.options,i=this.element.find(".ui-slider-handle"),s=[],n=e.values&&e.values.length||1;for(i.length>n&&(i.slice(n).remove(),i=i.slice(0,n)),t=i.length;t<n;t++)s.push("<span tabindex='0'></span>");this.handles=i.add(V(s.join("")).appendTo(this.element)),this._addClass(this.handles,"ui-slider-handle","ui-state-default"),this.handle=this.handles.eq(0),this.handles.each(function(t){V(this).data("ui-slider-handle-index",t).attr("tabIndex",0)})},_createRange:function(){var t=this.options;t.range?(!0===t.range&&(t.values?t.values.length&&2!==t.values.length?t.values=[t.values[0],t.values[0]]:Array.isArray(t.values)&&(t.values=t.values.slice(0)):t.values=[this._valueMin(),this._valueMin()]),this.range&&this.range.length?(this._removeClass(this.range,"ui-slider-range-min ui-slider-range-max"),this.range.css({left:"",bottom:""})):(this.range=V("<div>").appendTo(this.element),this._addClass(this.range,"ui-slider-range")),"min"!==t.range&&"max"!==t.range||this._addClass(this.range,"ui-slider-range-"+t.range)):(this.range&&this.range.remove(),this.range=null)},_setupEvents:function(){this._off(this.handles),this._on(this.handles,this._handleEvents),this._hoverable(this.handles),this._focusable(this.handles)},_destroy:function(){this.handles.remove(),this.range&&this.range.remove(),this._mouseDestroy()},_mouseCapture:function(t){var i,s,n,o,e,a,r=this,l=this.options;return!l.disabled&&(this.elementSize={width:this.element.outerWidth(),height:this.element.outerHeight()},this.elementOffset=this.element.offset(),a={x:t.pageX,y:t.pageY},i=this._normValueFromMouse(a),s=this._valueMax()-this._valueMin()+1,this.handles.each(function(t){var e=Math.abs(i-r.values(t));(e<s||s===e&&(t===r._lastChangedValue||r.values(t)===l.min))&&(s=e,n=V(this),o=t)}),!1!==this._start(t,o)&&(this._mouseSliding=!0,this._handleIndex=o,this._addClass(n,null,"ui-state-active"),n.trigger("focus"),e=n.offset(),a=!V(t.target).parents().addBack().is(".ui-slider-handle"),this._clickOffset=a?{left:0,top:0}:{left:t.pageX-e.left-n.width()/2,top:t.pageY-e.top-n.height()/2-(parseInt(n.css("borderTopWidth"),10)||0)-(parseInt(n.css("borderBottomWidth"),10)||0)+(parseInt(n.css("marginTop"),10)||0)},this.handles.hasClass("ui-state-hover")||this._slide(t,o,i),this._animateOff=!0))},_mouseStart:function(){return!0},_mouseDrag:function(t){var e={x:t.pageX,y:t.pageY},e=this._normValueFromMouse(e);return this._slide(t,this._handleIndex,e),!1},_mouseStop:function(t){return this._removeClass(this.handles,null,"ui-state-active"),this._mouseSliding=!1,this._stop(t,this._handleIndex),this._change(t,this._handleIndex),this._handleIndex=null,this._clickOffset=null,this._animateOff=!1},_detectOrientation:function(){this.orientation="vertical"===this.options.orientation?"vertical":"horizontal"},_normValueFromMouse:function(t){var e,t="horizontal"===this.orientation?(e=this.elementSize.width,t.x-this.elementOffset.left-(this._clickOffset?this._clickOffset.left:0)):(e=this.elementSize.height,t.y-this.elementOffset.top-(this._clickOffset?this._clickOffset.top:0)),t=t/e;return(t=1<t?1:t)<0&&(t=0),"vertical"===this.orientation&&(t=1-t),e=this._valueMax()-this._valueMin(),e=this._valueMin()+t*e,this._trimAlignValue(e)},_uiHash:function(t,e,i){var s={handle:this.handles[t],handleIndex:t,value:void 0!==e?e:this.value()};return this._hasMultipleValues()&&(s.value=void 0!==e?e:this.values(t),s.values=i||this.values()),s},_hasMultipleValues:function(){return this.options.values&&this.options.values.length},_start:function(t,e){return this._trigger("start",t,this._uiHash(e))},_slide:function(t,e,i){var s,n=this.value(),o=this.values();this._hasMultipleValues()&&(s=this.values(e?0:1),n=this.values(e),2===this.options.values.length&&!0===this.options.range&&(i=0===e?Math.min(s,i):Math.max(s,i)),o[e]=i),i!==n&&!1!==this._trigger("slide",t,this._uiHash(e,i,o))&&(this._hasMultipleValues()?this.values(e,i):this.value(i))},_stop:function(t,e){this._trigger("stop",t,this._uiHash(e))},_change:function(t,e){this._keySliding||this._mouseSliding||(this._lastChangedValue=e,this._trigger("change",t,this._uiHash(e)))},value:function(t){return arguments.length?(this.options.value=this._trimAlignValue(t),this._refreshValue(),void this._change(null,0)):this._value()},values:function(t,e){var i,s,n;if(1<arguments.length)return this.options.values[t]=this._trimAlignValue(e),this._refreshValue(),void this._change(null,t);if(!arguments.length)return this._values();if(!Array.isArray(t))return this._hasMultipleValues()?this._values(t):this.value();for(i=this.options.values,s=t,n=0;n<i.length;n+=1)i[n]=this._trimAlignValue(s[n]),this._change(null,n);this._refreshValue()},_setOption:function(t,e){var i,s=0;switch("range"===t&&!0===this.options.range&&("min"===e?(this.options.value=this._values(0),this.options.values=null):"max"===e&&(this.options.value=this._values(this.options.values.length-1),this.options.values=null)),Array.isArray(this.options.values)&&(s=this.options.values.length),this._super(t,e),t){case"orientation":this._detectOrientation(),this._removeClass("ui-slider-horizontal ui-slider-vertical")._addClass("ui-slider-"+this.orientation),this._refreshValue(),this.options.range&&this._refreshRange(e),this.handles.css("horizontal"===e?"bottom":"left","");break;case"value":this._animateOff=!0,this._refreshValue(),this._change(null,0),this._animateOff=!1;break;case"values":for(this._animateOff=!0,this._refreshValue(),i=s-1;0<=i;i--)this._change(null,i);this._animateOff=!1;break;case"step":case"min":case"max":this._animateOff=!0,this._calculateNewMax(),this._refreshValue(),this._animateOff=!1;break;case"range":this._animateOff=!0,this._refresh(),this._animateOff=!1}},_setOptionDisabled:function(t){this._super(t),this._toggleClass(null,"ui-state-disabled",!!t)},_value:function(){var t=this.options.value;return t=this._trimAlignValue(t)},_values:function(t){var e,i;if(arguments.length)return t=this.options.values[t],t=this._trimAlignValue(t);if(this._hasMultipleValues()){for(e=this.options.values.slice(),i=0;i<e.length;i+=1)e[i]=this._trimAlignValue(e[i]);return e}return[]},_trimAlignValue:function(t){if(t<=this._valueMin())return this._valueMin();if(t>=this._valueMax())return this._valueMax();var e=0<this.options.step?this.options.step:1,i=(t-this._valueMin())%e,t=t-i;return 2*Math.abs(i)>=e&&(t+=0<i?e:-e),parseFloat(t.toFixed(5))},_calculateNewMax:function(){var t=this.options.max,e=this._valueMin(),i=this.options.step;(t=Math.round((t-e)/i)*i+e)>this.options.max&&(t-=i),this.max=parseFloat(t.toFixed(this._precision()))},_precision:function(){var t=this._precisionOf(this.options.step);return t=null!==this.options.min?Math.max(t,this._precisionOf(this.options.min)):t},_precisionOf:function(t){var e=t.toString(),t=e.indexOf(".");return-1===t?0:e.length-t-1},_valueMin:function(){return this.options.min},_valueMax:function(){return this.max},_refreshRange:function(t){"vertical"===t&&this.range.css({width:"",left:""}),"horizontal"===t&&this.range.css({height:"",bottom:""})},_refreshValue:function(){var e,i,t,s,n,o=this.options.range,a=this.options,r=this,l=!this._animateOff&&a.animate,h={};this._hasMultipleValues()?this.handles.each(function(t){i=(r.values(t)-r._valueMin())/(r._valueMax()-r._valueMin())*100,h["horizontal"===r.orientation?"left":"bottom"]=i+"%",V(this).stop(1,1)[l?"animate":"css"](h,a.animate),!0===r.options.range&&("horizontal"===r.orientation?(0===t&&r.range.stop(1,1)[l?"animate":"css"]({left:i+"%"},a.animate),1===t&&r.range[l?"animate":"css"]({width:i-e+"%"},{queue:!1,duration:a.animate})):(0===t&&r.range.stop(1,1)[l?"animate":"css"]({bottom:i+"%"},a.animate),1===t&&r.range[l?"animate":"css"]({height:i-e+"%"},{queue:!1,duration:a.animate}))),e=i}):(t=this.value(),s=this._valueMin(),n=this._valueMax(),i=n!==s?(t-s)/(n-s)*100:0,h["horizontal"===this.orientation?"left":"bottom"]=i+"%",this.handle.stop(1,1)[l?"animate":"css"](h,a.animate),"min"===o&&"horizontal"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({width:i+"%"},a.animate),"max"===o&&"horizontal"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({width:100-i+"%"},a.animate),"min"===o&&"vertical"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({height:i+"%"},a.animate),"max"===o&&"vertical"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({height:100-i+"%"},a.animate))},_handleEvents:{keydown:function(t){var e,i,s,n=V(t.target).data("ui-slider-handle-index");switch(t.keyCode){case V.ui.keyCode.HOME:case V.ui.keyCode.END:case V.ui.keyCode.PAGE_UP:case V.ui.keyCode.PAGE_DOWN:case V.ui.keyCode.UP:case V.ui.keyCode.RIGHT:case V.ui.keyCode.DOWN:case V.ui.keyCode.LEFT:if(t.preventDefault(),!this._keySliding&&(this._keySliding=!0,this._addClass(V(t.target),null,"ui-state-active"),!1===this._start(t,n)))return}switch(s=this.options.step,e=i=this._hasMultipleValues()?this.values(n):this.value(),t.keyCode){case V.ui.keyCode.HOME:i=this._valueMin();break;case V.ui.keyCode.END:i=this._valueMax();break;case V.ui.keyCode.PAGE_UP:i=this._trimAlignValue(e+(this._valueMax()-this._valueMin())/this.numPages);break;case V.ui.keyCode.PAGE_DOWN:i=this._trimAlignValue(e-(this._valueMax()-this._valueMin())/this.numPages);break;case V.ui.keyCode.UP:case V.ui.keyCode.RIGHT:if(e===this._valueMax())return;i=this._trimAlignValue(e+s);break;case V.ui.keyCode.DOWN:case V.ui.keyCode.LEFT:if(e===this._valueMin())return;i=this._trimAlignValue(e-s)}this._slide(t,n,i)},keyup:function(t){var e=V(t.target).data("ui-slider-handle-index");this._keySliding&&(this._keySliding=!1,this._stop(t,e),this._change(t,e),this._removeClass(V(t.target),null,"ui-state-active"))}}}),V.widget("ui.sortable",V.ui.mouse,{version:"1.13.2",widgetEventPrefix:"sort",ready:!1,options:{appendTo:"parent",axis:!1,connectWith:!1,containment:!1,cursor:"auto",cursorAt:!1,dropOnEmpty:!0,forcePlaceholderSize:!1,forceHelperSize:!1,grid:!1,handle:!1,helper:"original",items:"> *",opacity:!1,placeholder:!1,revert:!1,scroll:!0,scrollSensitivity:20,scrollSpeed:20,scope:"default",tolerance:"intersect",zIndex:1e3,activate:null,beforeStop:null,change:null,deactivate:null,out:null,over:null,receive:null,remove:null,sort:null,start:null,stop:null,update:null},_isOverAxis:function(t,e,i){return e<=t&&t<e+i},_isFloating:function(t){return/left|right/.test(t.css("float"))||/inline|table-cell/.test(t.css("display"))},_create:function(){this.containerCache={},this._addClass("ui-sortable"),this.refresh(),this.offset=this.element.offset(),this._mouseInit(),this._setHandleClassName(),this.ready=!0},_setOption:function(t,e){this._super(t,e),"handle"===t&&this._setHandleClassName()},_setHandleClassName:function(){var t=this;this._removeClass(this.element.find(".ui-sortable-handle"),"ui-sortable-handle"),V.each(this.items,function(){t._addClass(this.instance.options.handle?this.item.find(this.instance.options.handle):this.item,"ui-sortable-handle")})},_destroy:function(){this._mouseDestroy();for(var t=this.items.length-1;0<=t;t--)this.items[t].item.removeData(this.widgetName+"-item");return this},_mouseCapture:function(t,e){var i=null,s=!1,n=this;return!this.reverting&&(!this.options.disabled&&"static"!==this.options.type&&(this._refreshItems(t),V(t.target).parents().each(function(){if(V.data(this,n.widgetName+"-item")===n)return i=V(this),!1}),!!(i=V.data(t.target,n.widgetName+"-item")===n?V(t.target):i)&&(!(this.options.handle&&!e&&(V(this.options.handle,i).find("*").addBack().each(function(){this===t.target&&(s=!0)}),!s))&&(this.currentItem=i,this._removeCurrentsFromItems(),!0))))},_mouseStart:function(t,e,i){var s,n,o=this.options;if((this.currentContainer=this).refreshPositions(),this.appendTo=V("parent"!==o.appendTo?o.appendTo:this.currentItem.parent()),this.helper=this._createHelper(t),this._cacheHelperProportions(),this._cacheMargins(),this.offset=this.currentItem.offset(),this.offset={top:this.offset.top-this.margins.top,left:this.offset.left-this.margins.left},V.extend(this.offset,{click:{left:t.pageX-this.offset.left,top:t.pageY-this.offset.top},relative:this._getRelativeOffset()}),this.helper.css("position","absolute"),this.cssPosition=this.helper.css("position"),o.cursorAt&&this._adjustOffsetFromHelper(o.cursorAt),this.domPosition={prev:this.currentItem.prev()[0],parent:this.currentItem.parent()[0]},this.helper[0]!==this.currentItem[0]&&this.currentItem.hide(),this._createPlaceholder(),this.scrollParent=this.placeholder.scrollParent(),V.extend(this.offset,{parent:this._getParentOffset()}),o.containment&&this._setContainment(),o.cursor&&"auto"!==o.cursor&&(n=this.document.find("body"),this.storedCursor=n.css("cursor"),n.css("cursor",o.cursor),this.storedStylesheet=V("<style>*{ cursor: "+o.cursor+" !important; }</style>").appendTo(n)),o.zIndex&&(this.helper.css("zIndex")&&(this._storedZIndex=this.helper.css("zIndex")),this.helper.css("zIndex",o.zIndex)),o.opacity&&(this.helper.css("opacity")&&(this._storedOpacity=this.helper.css("opacity")),this.helper.css("opacity",o.opacity)),this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName&&(this.overflowOffset=this.scrollParent.offset()),this._trigger("start",t,this._uiHash()),this._preserveHelperProportions||this._cacheHelperProportions(),!i)for(s=this.containers.length-1;0<=s;s--)this.containers[s]._trigger("activate",t,this._uiHash(this));return V.ui.ddmanager&&(V.ui.ddmanager.current=this),V.ui.ddmanager&&!o.dropBehaviour&&V.ui.ddmanager.prepareOffsets(this,t),this.dragging=!0,this._addClass(this.helper,"ui-sortable-helper"),this.helper.parent().is(this.appendTo)||(this.helper.detach().appendTo(this.appendTo),this.offset.parent=this._getParentOffset()),this.position=this.originalPosition=this._generatePosition(t),this.originalPageX=t.pageX,this.originalPageY=t.pageY,this.lastPositionAbs=this.positionAbs=this._convertPositionTo("absolute"),this._mouseDrag(t),!0},_scroll:function(t){var e=this.options,i=!1;return this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName?(this.overflowOffset.top+this.scrollParent[0].offsetHeight-t.pageY<e.scrollSensitivity?this.scrollParent[0].scrollTop=i=this.scrollParent[0].scrollTop+e.scrollSpeed:t.pageY-this.overflowOffset.top<e.scrollSensitivity&&(this.scrollParent[0].scrollTop=i=this.scrollParent[0].scrollTop-e.scrollSpeed),this.overflowOffset.left+this.scrollParent[0].offsetWidth-t.pageX<e.scrollSensitivity?this.scrollParent[0].scrollLeft=i=this.scrollParent[0].scrollLeft+e.scrollSpeed:t.pageX-this.overflowOffset.left<e.scrollSensitivity&&(this.scrollParent[0].scrollLeft=i=this.scrollParent[0].scrollLeft-e.scrollSpeed)):(t.pageY-this.document.scrollTop()<e.scrollSensitivity?i=this.document.scrollTop(this.document.scrollTop()-e.scrollSpeed):this.window.height()-(t.pageY-this.document.scrollTop())<e.scrollSensitivity&&(i=this.document.scrollTop(this.document.scrollTop()+e.scrollSpeed)),t.pageX-this.document.scrollLeft()<e.scrollSensitivity?i=this.document.scrollLeft(this.document.scrollLeft()-e.scrollSpeed):this.window.width()-(t.pageX-this.document.scrollLeft())<e.scrollSensitivity&&(i=this.document.scrollLeft(this.document.scrollLeft()+e.scrollSpeed))),i},_mouseDrag:function(t){var e,i,s,n,o=this.options;for(this.position=this._generatePosition(t),this.positionAbs=this._convertPositionTo("absolute"),this.options.axis&&"y"===this.options.axis||(this.helper[0].style.left=this.position.left+"px"),this.options.axis&&"x"===this.options.axis||(this.helper[0].style.top=this.position.top+"px"),o.scroll&&!1!==this._scroll(t)&&(this._refreshItemPositions(!0),V.ui.ddmanager&&!o.dropBehaviour&&V.ui.ddmanager.prepareOffsets(this,t)),this.dragDirection={vertical:this._getDragVerticalDirection(),horizontal:this._getDragHorizontalDirection()},e=this.items.length-1;0<=e;e--)if(s=(i=this.items[e]).item[0],(n=this._intersectsWithPointer(i))&&i.instance===this.currentContainer&&!(s===this.currentItem[0]||this.placeholder[1===n?"next":"prev"]()[0]===s||V.contains(this.placeholder[0],s)||"semi-dynamic"===this.options.type&&V.contains(this.element[0],s))){if(this.direction=1===n?"down":"up","pointer"!==this.options.tolerance&&!this._intersectsWithSides(i))break;this._rearrange(t,i),this._trigger("change",t,this._uiHash());break}return this._contactContainers(t),V.ui.ddmanager&&V.ui.ddmanager.drag(this,t),this._trigger("sort",t,this._uiHash()),this.lastPositionAbs=this.positionAbs,!1},_mouseStop:function(t,e){var i,s,n,o;if(t)return V.ui.ddmanager&&!this.options.dropBehaviour&&V.ui.ddmanager.drop(this,t),this.options.revert?(s=(i=this).placeholder.offset(),o={},(n=this.options.axis)&&"x"!==n||(o.left=s.left-this.offset.parent.left-this.margins.left+(this.offsetParent[0]===this.document[0].body?0:this.offsetParent[0].scrollLeft)),n&&"y"!==n||(o.top=s.top-this.offset.parent.top-this.margins.top+(this.offsetParent[0]===this.document[0].body?0:this.offsetParent[0].scrollTop)),this.reverting=!0,V(this.helper).animate(o,parseInt(this.options.revert,10)||500,function(){i._clear(t)})):this._clear(t,e),!1},cancel:function(){if(this.dragging){this._mouseUp(new V.Event("mouseup",{target:null})),"original"===this.options.helper?(this.currentItem.css(this._storedCSS),this._removeClass(this.currentItem,"ui-sortable-helper")):this.currentItem.show();for(var t=this.containers.length-1;0<=t;t--)this.containers[t]._trigger("deactivate",null,this._uiHash(this)),this.containers[t].containerCache.over&&(this.containers[t]._trigger("out",null,this._uiHash(this)),this.containers[t].containerCache.over=0)}return this.placeholder&&(this.placeholder[0].parentNode&&this.placeholder[0].parentNode.removeChild(this.placeholder[0]),"original"!==this.options.helper&&this.helper&&this.helper[0].parentNode&&this.helper.remove(),V.extend(this,{helper:null,dragging:!1,reverting:!1,_noFinalSort:null}),this.domPosition.prev?V(this.domPosition.prev).after(this.currentItem):V(this.domPosition.parent).prepend(this.currentItem)),this},serialize:function(e){var t=this._getItemsAsjQuery(e&&e.connected),i=[];return e=e||{},V(t).each(function(){var t=(V(e.item||this).attr(e.attribute||"id")||"").match(e.expression||/(.+)[\-=_](.+)/);t&&i.push((e.key||t[1]+"[]")+"="+(e.key&&e.expression?t[1]:t[2]))}),!i.length&&e.key&&i.push(e.key+"="),i.join("&")},toArray:function(t){var e=this._getItemsAsjQuery(t&&t.connected),i=[];return t=t||{},e.each(function(){i.push(V(t.item||this).attr(t.attribute||"id")||"")}),i},_intersectsWith:function(t){var e=this.positionAbs.left,i=e+this.helperProportions.width,s=this.positionAbs.top,n=s+this.helperProportions.height,o=t.left,a=o+t.width,r=t.top,l=r+t.height,h=this.offset.click.top,c=this.offset.click.left,h="x"===this.options.axis||r<s+h&&s+h<l,c="y"===this.options.axis||o<e+c&&e+c<a;return"pointer"===this.options.tolerance||this.options.forcePointerForContainers||"pointer"!==this.options.tolerance&&this.helperProportions[this.floating?"width":"height"]>t[this.floating?"width":"height"]?h&&c:o<e+this.helperProportions.width/2&&i-this.helperProportions.width/2<a&&r<s+this.helperProportions.height/2&&n-this.helperProportions.height/2<l},_intersectsWithPointer:function(t){var e="x"===this.options.axis||this._isOverAxis(this.positionAbs.top+this.offset.click.top,t.top,t.height),t="y"===this.options.axis||this._isOverAxis(this.positionAbs.left+this.offset.click.left,t.left,t.width);return!(!e||!t)&&(e=this.dragDirection.vertical,t=this.dragDirection.horizontal,this.floating?"right"===t||"down"===e?2:1:e&&("down"===e?2:1))},_intersectsWithSides:function(t){var e=this._isOverAxis(this.positionAbs.top+this.offset.click.top,t.top+t.height/2,t.height),i=this._isOverAxis(this.positionAbs.left+this.offset.click.left,t.left+t.width/2,t.width),s=this.dragDirection.vertical,t=this.dragDirection.horizontal;return this.floating&&t?"right"===t&&i||"left"===t&&!i:s&&("down"===s&&e||"up"===s&&!e)},_getDragVerticalDirection:function(){var t=this.positionAbs.top-this.lastPositionAbs.top;return 0!=t&&(0<t?"down":"up")},_getDragHorizontalDirection:function(){var t=this.positionAbs.left-this.lastPositionAbs.left;return 0!=t&&(0<t?"right":"left")},refresh:function(t){return this._refreshItems(t),this._setHandleClassName(),this.refreshPositions(),this},_connectWith:function(){var t=this.options;return t.connectWith.constructor===String?[t.connectWith]:t.connectWith},_getItemsAsjQuery:function(t){var e,i,s,n,o=[],a=[],r=this._connectWith();if(r&&t)for(e=r.length-1;0<=e;e--)for(i=(s=V(r[e],this.document[0])).length-1;0<=i;i--)(n=V.data(s[i],this.widgetFullName))&&n!==this&&!n.options.disabled&&a.push(["function"==typeof n.options.items?n.options.items.call(n.element):V(n.options.items,n.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),n]);function l(){o.push(this)}for(a.push(["function"==typeof this.options.items?this.options.items.call(this.element,null,{options:this.options,item:this.currentItem}):V(this.options.items,this.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),this]),e=a.length-1;0<=e;e--)a[e][0].each(l);return V(o)},_removeCurrentsFromItems:function(){var i=this.currentItem.find(":data("+this.widgetName+"-item)");this.items=V.grep(this.items,function(t){for(var e=0;e<i.length;e++)if(i[e]===t.item[0])return!1;return!0})},_refreshItems:function(t){this.items=[],this.containers=[this];var e,i,s,n,o,a,r,l,h=this.items,c=[["function"==typeof this.options.items?this.options.items.call(this.element[0],t,{item:this.currentItem}):V(this.options.items,this.element),this]],u=this._connectWith();if(u&&this.ready)for(e=u.length-1;0<=e;e--)for(i=(s=V(u[e],this.document[0])).length-1;0<=i;i--)(n=V.data(s[i],this.widgetFullName))&&n!==this&&!n.options.disabled&&(c.push(["function"==typeof n.options.items?n.options.items.call(n.element[0],t,{item:this.currentItem}):V(n.options.items,n.element),n]),this.containers.push(n));for(e=c.length-1;0<=e;e--)for(o=c[e][1],l=(a=c[e][i=0]).length;i<l;i++)(r=V(a[i])).data(this.widgetName+"-item",o),h.push({item:r,instance:o,width:0,height:0,left:0,top:0})},_refreshItemPositions:function(t){for(var e,i,s=this.items.length-1;0<=s;s--)e=this.items[s],this.currentContainer&&e.instance!==this.currentContainer&&e.item[0]!==this.currentItem[0]||(i=this.options.toleranceElement?V(this.options.toleranceElement,e.item):e.item,t||(e.width=i.outerWidth(),e.height=i.outerHeight()),i=i.offset(),e.left=i.left,e.top=i.top)},refreshPositions:function(t){var e,i;if(this.floating=!!this.items.length&&("x"===this.options.axis||this._isFloating(this.items[0].item)),this.offsetParent&&this.helper&&(this.offset.parent=this._getParentOffset()),this._refreshItemPositions(t),this.options.custom&&this.options.custom.refreshContainers)this.options.custom.refreshContainers.call(this);else for(e=this.containers.length-1;0<=e;e--)i=this.containers[e].element.offset(),this.containers[e].containerCache.left=i.left,this.containers[e].containerCache.top=i.top,this.containers[e].containerCache.width=this.containers[e].element.outerWidth(),this.containers[e].containerCache.height=this.containers[e].element.outerHeight();return this},_createPlaceholder:function(i){var s,n,o=(i=i||this).options;o.placeholder&&o.placeholder.constructor!==String||(s=o.placeholder,n=i.currentItem[0].nodeName.toLowerCase(),o.placeholder={element:function(){var t=V("<"+n+">",i.document[0]);return i._addClass(t,"ui-sortable-placeholder",s||i.currentItem[0].className)._removeClass(t,"ui-sortable-helper"),"tbody"===n?i._createTrPlaceholder(i.currentItem.find("tr").eq(0),V("<tr>",i.document[0]).appendTo(t)):"tr"===n?i._createTrPlaceholder(i.currentItem,t):"img"===n&&t.attr("src",i.currentItem.attr("src")),s||t.css("visibility","hidden"),t},update:function(t,e){s&&!o.forcePlaceholderSize||(e.height()&&(!o.forcePlaceholderSize||"tbody"!==n&&"tr"!==n)||e.height(i.currentItem.innerHeight()-parseInt(i.currentItem.css("paddingTop")||0,10)-parseInt(i.currentItem.css("paddingBottom")||0,10)),e.width()||e.width(i.currentItem.innerWidth()-parseInt(i.currentItem.css("paddingLeft")||0,10)-parseInt(i.currentItem.css("paddingRight")||0,10)))}}),i.placeholder=V(o.placeholder.element.call(i.element,i.currentItem)),i.currentItem.after(i.placeholder),o.placeholder.update(i,i.placeholder)},_createTrPlaceholder:function(t,e){var i=this;t.children().each(function(){V("<td>&#160;</td>",i.document[0]).attr("colspan",V(this).attr("colspan")||1).appendTo(e)})},_contactContainers:function(t){for(var e,i,s,n,o,a,r,l,h,c=null,u=null,d=this.containers.length-1;0<=d;d--)V.contains(this.currentItem[0],this.containers[d].element[0])||(this._intersectsWith(this.containers[d].containerCache)?c&&V.contains(this.containers[d].element[0],c.element[0])||(c=this.containers[d],u=d):this.containers[d].containerCache.over&&(this.containers[d]._trigger("out",t,this._uiHash(this)),this.containers[d].containerCache.over=0));if(c)if(1===this.containers.length)this.containers[u].containerCache.over||(this.containers[u]._trigger("over",t,this._uiHash(this)),this.containers[u].containerCache.over=1);else{for(i=1e4,s=null,n=(l=c.floating||this._isFloating(this.currentItem))?"left":"top",o=l?"width":"height",h=l?"pageX":"pageY",e=this.items.length-1;0<=e;e--)V.contains(this.containers[u].element[0],this.items[e].item[0])&&this.items[e].item[0]!==this.currentItem[0]&&(a=this.items[e].item.offset()[n],r=!1,t[h]-a>this.items[e][o]/2&&(r=!0),Math.abs(t[h]-a)<i&&(i=Math.abs(t[h]-a),s=this.items[e],this.direction=r?"up":"down"));(s||this.options.dropOnEmpty)&&(this.currentContainer!==this.containers[u]?(s?this._rearrange(t,s,null,!0):this._rearrange(t,null,this.containers[u].element,!0),this._trigger("change",t,this._uiHash()),this.containers[u]._trigger("change",t,this._uiHash(this)),this.currentContainer=this.containers[u],this.options.placeholder.update(this.currentContainer,this.placeholder),this.scrollParent=this.placeholder.scrollParent(),this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName&&(this.overflowOffset=this.scrollParent.offset()),this.containers[u]._trigger("over",t,this._uiHash(this)),this.containers[u].containerCache.over=1):this.currentContainer.containerCache.over||(this.containers[u]._trigger("over",t,this._uiHash()),this.currentContainer.containerCache.over=1))}},_createHelper:function(t){var e=this.options,t="function"==typeof e.helper?V(e.helper.apply(this.element[0],[t,this.currentItem])):"clone"===e.helper?this.currentItem.clone():this.currentItem;return t.parents("body").length||this.appendTo[0].appendChild(t[0]),t[0]===this.currentItem[0]&&(this._storedCSS={width:this.currentItem[0].style.width,height:this.currentItem[0].style.height,position:this.currentItem.css("position"),top:this.currentItem.css("top"),left:this.currentItem.css("left")}),t[0].style.width&&!e.forceHelperSize||t.width(this.currentItem.width()),t[0].style.height&&!e.forceHelperSize||t.height(this.currentItem.height()),t},_adjustOffsetFromHelper:function(t){"string"==typeof t&&(t=t.split(" ")),"left"in(t=Array.isArray(t)?{left:+t[0],top:+t[1]||0}:t)&&(this.offset.click.left=t.left+this.margins.left),"right"in t&&(this.offset.click.left=this.helperProportions.width-t.right+this.margins.left),"top"in t&&(this.offset.click.top=t.top+this.margins.top),"bottom"in t&&(this.offset.click.top=this.helperProportions.height-t.bottom+this.margins.top)},_getParentOffset:function(){this.offsetParent=this.helper.offsetParent();var t=this.offsetParent.offset();return"absolute"===this.cssPosition&&this.scrollParent[0]!==this.document[0]&&V.contains(this.scrollParent[0],this.offsetParent[0])&&(t.left+=this.scrollParent.scrollLeft(),t.top+=this.scrollParent.scrollTop()),{top:(t=this.offsetParent[0]===this.document[0].body||this.offsetParent[0].tagName&&"html"===this.offsetParent[0].tagName.toLowerCase()&&V.ui.ie?{top:0,left:0}:t).top+(parseInt(this.offsetParent.css("borderTopWidth"),10)||0),left:t.left+(parseInt(this.offsetParent.css("borderLeftWidth"),10)||0)}},_getRelativeOffset:function(){if("relative"!==this.cssPosition)return{top:0,left:0};var t=this.currentItem.position();return{top:t.top-(parseInt(this.helper.css("top"),10)||0)+this.scrollParent.scrollTop(),left:t.left-(parseInt(this.helper.css("left"),10)||0)+this.scrollParent.scrollLeft()}},_cacheMargins:function(){this.margins={left:parseInt(this.currentItem.css("marginLeft"),10)||0,top:parseInt(this.currentItem.css("marginTop"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var t,e,i=this.options;"parent"===i.containment&&(i.containment=this.helper[0].parentNode),"document"!==i.containment&&"window"!==i.containment||(this.containment=[0-this.offset.relative.left-this.offset.parent.left,0-this.offset.relative.top-this.offset.parent.top,"document"===i.containment?this.document.width():this.window.width()-this.helperProportions.width-this.margins.left,("document"===i.containment?this.document.height()||document.body.parentNode.scrollHeight:this.window.height()||this.document[0].body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top]),/^(document|window|parent)$/.test(i.containment)||(t=V(i.containment)[0],e=V(i.containment).offset(),i="hidden"!==V(t).css("overflow"),this.containment=[e.left+(parseInt(V(t).css("borderLeftWidth"),10)||0)+(parseInt(V(t).css("paddingLeft"),10)||0)-this.margins.left,e.top+(parseInt(V(t).css("borderTopWidth"),10)||0)+(parseInt(V(t).css("paddingTop"),10)||0)-this.margins.top,e.left+(i?Math.max(t.scrollWidth,t.offsetWidth):t.offsetWidth)-(parseInt(V(t).css("borderLeftWidth"),10)||0)-(parseInt(V(t).css("paddingRight"),10)||0)-this.helperProportions.width-this.margins.left,e.top+(i?Math.max(t.scrollHeight,t.offsetHeight):t.offsetHeight)-(parseInt(V(t).css("borderTopWidth"),10)||0)-(parseInt(V(t).css("paddingBottom"),10)||0)-this.helperProportions.height-this.margins.top])},_convertPositionTo:function(t,e){e=e||this.position;var i="absolute"===t?1:-1,s="absolute"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&V.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,t=/(html|body)/i.test(s[0].tagName);return{top:e.top+this.offset.relative.top*i+this.offset.parent.top*i-("fixed"===this.cssPosition?-this.scrollParent.scrollTop():t?0:s.scrollTop())*i,left:e.left+this.offset.relative.left*i+this.offset.parent.left*i-("fixed"===this.cssPosition?-this.scrollParent.scrollLeft():t?0:s.scrollLeft())*i}},_generatePosition:function(t){var e=this.options,i=t.pageX,s=t.pageY,n="absolute"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&V.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,o=/(html|body)/i.test(n[0].tagName);return"relative"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&this.scrollParent[0]!==this.offsetParent[0]||(this.offset.relative=this._getRelativeOffset()),this.originalPosition&&(this.containment&&(t.pageX-this.offset.click.left<this.containment[0]&&(i=this.containment[0]+this.offset.click.left),t.pageY-this.offset.click.top<this.containment[1]&&(s=this.containment[1]+this.offset.click.top),t.pageX-this.offset.click.left>this.containment[2]&&(i=this.containment[2]+this.offset.click.left),t.pageY-this.offset.click.top>this.containment[3]&&(s=this.containment[3]+this.offset.click.top)),e.grid&&(t=this.originalPageY+Math.round((s-this.originalPageY)/e.grid[1])*e.grid[1],s=!this.containment||t-this.offset.click.top>=this.containment[1]&&t-this.offset.click.top<=this.containment[3]?t:t-this.offset.click.top>=this.containment[1]?t-e.grid[1]:t+e.grid[1],t=this.originalPageX+Math.round((i-this.originalPageX)/e.grid[0])*e.grid[0],i=!this.containment||t-this.offset.click.left>=this.containment[0]&&t-this.offset.click.left<=this.containment[2]?t:t-this.offset.click.left>=this.containment[0]?t-e.grid[0]:t+e.grid[0])),{top:s-this.offset.click.top-this.offset.relative.top-this.offset.parent.top+("fixed"===this.cssPosition?-this.scrollParent.scrollTop():o?0:n.scrollTop()),left:i-this.offset.click.left-this.offset.relative.left-this.offset.parent.left+("fixed"===this.cssPosition?-this.scrollParent.scrollLeft():o?0:n.scrollLeft())}},_rearrange:function(t,e,i,s){i?i[0].appendChild(this.placeholder[0]):e.item[0].parentNode.insertBefore(this.placeholder[0],"down"===this.direction?e.item[0]:e.item[0].nextSibling),this.counter=this.counter?++this.counter:1;var n=this.counter;this._delay(function(){n===this.counter&&this.refreshPositions(!s)})},_clear:function(t,e){this.reverting=!1;var i,s=[];if(!this._noFinalSort&&this.currentItem.parent().length&&this.placeholder.before(this.currentItem),this._noFinalSort=null,this.helper[0]===this.currentItem[0]){for(i in this._storedCSS)"auto"!==this._storedCSS[i]&&"static"!==this._storedCSS[i]||(this._storedCSS[i]="");this.currentItem.css(this._storedCSS),this._removeClass(this.currentItem,"ui-sortable-helper")}else this.currentItem.show();function n(e,i,s){return function(t){s._trigger(e,t,i._uiHash(i))}}for(this.fromOutside&&!e&&s.push(function(t){this._trigger("receive",t,this._uiHash(this.fromOutside))}),!this.fromOutside&&this.domPosition.prev===this.currentItem.prev().not(".ui-sortable-helper")[0]&&this.domPosition.parent===this.currentItem.parent()[0]||e||s.push(function(t){this._trigger("update",t,this._uiHash())}),this!==this.currentContainer&&(e||(s.push(function(t){this._trigger("remove",t,this._uiHash())}),s.push(function(e){return function(t){e._trigger("receive",t,this._uiHash(this))}}.call(this,this.currentContainer)),s.push(function(e){return function(t){e._trigger("update",t,this._uiHash(this))}}.call(this,this.currentContainer)))),i=this.containers.length-1;0<=i;i--)e||s.push(n("deactivate",this,this.containers[i])),this.containers[i].containerCache.over&&(s.push(n("out",this,this.containers[i])),this.containers[i].containerCache.over=0);if(this.storedCursor&&(this.document.find("body").css("cursor",this.storedCursor),this.storedStylesheet.remove()),this._storedOpacity&&this.helper.css("opacity",this._storedOpacity),this._storedZIndex&&this.helper.css("zIndex","auto"===this._storedZIndex?"":this._storedZIndex),this.dragging=!1,e||this._trigger("beforeStop",t,this._uiHash()),this.placeholder[0].parentNode.removeChild(this.placeholder[0]),this.cancelHelperRemoval||(this.helper[0]!==this.currentItem[0]&&this.helper.remove(),this.helper=null),!e){for(i=0;i<s.length;i++)s[i].call(this,t);this._trigger("stop",t,this._uiHash())}return this.fromOutside=!1,!this.cancelHelperRemoval},_trigger:function(){!1===V.Widget.prototype._trigger.apply(this,arguments)&&this.cancel()},_uiHash:function(t){var e=t||this;return{helper:e.helper,placeholder:e.placeholder||V([]),position:e.position,originalPosition:e.originalPosition,offset:e.positionAbs,item:e.currentItem,sender:t?t.element:null}}});function ht(e){return function(){var t=this.element.val();e.apply(this,arguments),this._refresh(),t!==this.element.val()&&this._trigger("change")}}V.widget("ui.spinner",{version:"1.13.2",defaultElement:"<input>",widgetEventPrefix:"spin",options:{classes:{"ui-spinner":"ui-corner-all","ui-spinner-down":"ui-corner-br","ui-spinner-up":"ui-corner-tr"},culture:null,icons:{down:"ui-icon-triangle-1-s",up:"ui-icon-triangle-1-n"},incremental:!0,max:null,min:null,numberFormat:null,page:10,step:1,change:null,spin:null,start:null,stop:null},_create:function(){this._setOption("max",this.options.max),this._setOption("min",this.options.min),this._setOption("step",this.options.step),""!==this.value()&&this._value(this.element.val(),!0),this._draw(),this._on(this._events),this._refresh(),this._on(this.window,{beforeunload:function(){this.element.removeAttr("autocomplete")}})},_getCreateOptions:function(){var s=this._super(),n=this.element;return V.each(["min","max","step"],function(t,e){var i=n.attr(e);null!=i&&i.length&&(s[e]=i)}),s},_events:{keydown:function(t){this._start(t)&&this._keydown(t)&&t.preventDefault()},keyup:"_stop",focus:function(){this.previous=this.element.val()},blur:function(t){this.cancelBlur?delete this.cancelBlur:(this._stop(),this._refresh(),this.previous!==this.element.val()&&this._trigger("change",t))},mousewheel:function(t,e){var i=V.ui.safeActiveElement(this.document[0]);if(this.element[0]===i&&e){if(!this.spinning&&!this._start(t))return!1;this._spin((0<e?1:-1)*this.options.step,t),clearTimeout(this.mousewheelTimer),this.mousewheelTimer=this._delay(function(){this.spinning&&this._stop(t)},100),t.preventDefault()}},"mousedown .ui-spinner-button":function(t){var e;function i(){this.element[0]===V.ui.safeActiveElement(this.document[0])||(this.element.trigger("focus"),this.previous=e,this._delay(function(){this.previous=e}))}e=this.element[0]===V.ui.safeActiveElement(this.document[0])?this.previous:this.element.val(),t.preventDefault(),i.call(this),this.cancelBlur=!0,this._delay(function(){delete this.cancelBlur,i.call(this)}),!1!==this._start(t)&&this._repeat(null,V(t.currentTarget).hasClass("ui-spinner-up")?1:-1,t)},"mouseup .ui-spinner-button":"_stop","mouseenter .ui-spinner-button":function(t){if(V(t.currentTarget).hasClass("ui-state-active"))return!1!==this._start(t)&&void this._repeat(null,V(t.currentTarget).hasClass("ui-spinner-up")?1:-1,t)},"mouseleave .ui-spinner-button":"_stop"},_enhance:function(){this.uiSpinner=this.element.attr("autocomplete","off").wrap("<span>").parent().append("<a></a><a></a>")},_draw:function(){this._enhance(),this._addClass(this.uiSpinner,"ui-spinner","ui-widget ui-widget-content"),this._addClass("ui-spinner-input"),this.element.attr("role","spinbutton"),this.buttons=this.uiSpinner.children("a").attr("tabIndex",-1).attr("aria-hidden",!0).button({classes:{"ui-button":""}}),this._removeClass(this.buttons,"ui-corner-all"),this._addClass(this.buttons.first(),"ui-spinner-button ui-spinner-up"),this._addClass(this.buttons.last(),"ui-spinner-button ui-spinner-down"),this.buttons.first().button({icon:this.options.icons.up,showLabel:!1}),this.buttons.last().button({icon:this.options.icons.down,showLabel:!1}),this.buttons.height()>Math.ceil(.5*this.uiSpinner.height())&&0<this.uiSpinner.height()&&this.uiSpinner.height(this.uiSpinner.height())},_keydown:function(t){var e=this.options,i=V.ui.keyCode;switch(t.keyCode){case i.UP:return this._repeat(null,1,t),!0;case i.DOWN:return this._repeat(null,-1,t),!0;case i.PAGE_UP:return this._repeat(null,e.page,t),!0;case i.PAGE_DOWN:return this._repeat(null,-e.page,t),!0}return!1},_start:function(t){return!(!this.spinning&&!1===this._trigger("start",t))&&(this.counter||(this.counter=1),this.spinning=!0)},_repeat:function(t,e,i){t=t||500,clearTimeout(this.timer),this.timer=this._delay(function(){this._repeat(40,e,i)},t),this._spin(e*this.options.step,i)},_spin:function(t,e){var i=this.value()||0;this.counter||(this.counter=1),i=this._adjustValue(i+t*this._increment(this.counter)),this.spinning&&!1===this._trigger("spin",e,{value:i})||(this._value(i),this.counter++)},_increment:function(t){var e=this.options.incremental;return e?"function"==typeof e?e(t):Math.floor(t*t*t/5e4-t*t/500+17*t/200+1):1},_precision:function(){var t=this._precisionOf(this.options.step);return t=null!==this.options.min?Math.max(t,this._precisionOf(this.options.min)):t},_precisionOf:function(t){var e=t.toString(),t=e.indexOf(".");return-1===t?0:e.length-t-1},_adjustValue:function(t){var e=this.options,i=null!==e.min?e.min:0,s=t-i;return 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width: 20%;
max-height: 90%;
overflow: auto;
margin-left: 2%;
position: fixed;
border: 1px solid #ccc;
border-radius: 6px;
}

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margin: 0;
padding: 0;
border: none;
line-height: 30px;
}

.tocify-header {
text-indent: 10px;
}

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text-indent: 20px;
display: none;
}

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}

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text-indent: 60px;
}

.tocify .tocify-item > a, .tocify .nav-list .nav-header {
margin: 0px;
}

.tocify .tocify-item a, .tocify .list-group-item {
padding: 5px;
}
.tocify .nav-pills > li {
float: none;
}


</style>
<script>/* jquery Tocify - v1.9.1 - 2013-10-22
 * http://www.gregfranko.com/jquery.tocify.js/
 * Copyright (c) 2013 Greg Franko; Licensed MIT */

// Immediately-Invoked Function Expression (IIFE) [Ben Alman Blog Post](http://benalman.com/news/2010/11/immediately-invoked-function-expression/) that calls another IIFE that contains all of the plugin logic.  I used this pattern so that anyone viewing this code would not have to scroll to the bottom of the page to view the local parameters that were passed to the main IIFE.
(function(tocify) {

    // ECMAScript 5 Strict Mode: [John Resig Blog Post](http://ejohn.org/blog/ecmascript-5-strict-mode-json-and-more/)
    "use strict";

    // Calls the second IIFE and locally passes in the global jQuery, window, and document objects
    tocify(window.jQuery, window, document);

  }

  // Locally passes in `jQuery`, the `window` object, the `document` object, and an `undefined` variable.  The `jQuery`, `window` and `document` objects are passed in locally, to improve performance, since javascript first searches for a variable match within the local variables set before searching the global variables set.  All of the global variables are also passed in locally to be minifier friendly. `undefined` can be passed in locally, because it is not a reserved word in JavaScript.
  (function($, window, document, undefined) {

    // ECMAScript 5 Strict Mode: [John Resig Blog Post](http://ejohn.org/blog/ecmascript-5-strict-mode-json-and-more/)
    "use strict";

    var tocClassName = "tocify",
      tocClass = "." + tocClassName,
      tocFocusClassName = "tocify-focus",
      tocHoverClassName = "tocify-hover",
      hideTocClassName = "tocify-hide",
      hideTocClass = "." + hideTocClassName,
      headerClassName = "tocify-header",
      headerClass = "." + headerClassName,
      subheaderClassName = "tocify-subheader",
      subheaderClass = "." + subheaderClassName,
      itemClassName = "tocify-item",
      itemClass = "." + itemClassName,
      extendPageClassName = "tocify-extend-page",
      extendPageClass = "." + extendPageClassName;

    // Calling the jQueryUI Widget Factory Method
    $.widget("toc.tocify", {

      //Plugin version
      version: "1.9.1",

      // These options will be used as defaults
      options: {

        // **context**: Accepts String: Any jQuery selector
        // The container element that holds all of the elements used to generate the table of contents
        context: "body",

        // **ignoreSelector**: Accepts String: Any jQuery selector
        // A selector to any element that would be matched by selectors that you wish to be ignored
        ignoreSelector: null,

        // **selectors**: Accepts an Array of Strings: Any jQuery selectors
        // The element's used to generate the table of contents.  The order is very important since it will determine the table of content's nesting structure
        selectors: "h1, h2, h3",

        // **showAndHide**: Accepts a boolean: true or false
        // Used to determine if elements should be shown and hidden
        showAndHide: true,

        // **showEffect**: Accepts String: "none", "fadeIn", "show", or "slideDown"
        // Used to display any of the table of contents nested items
        showEffect: "slideDown",

        // **showEffectSpeed**: Accepts Number (milliseconds) or String: "slow", "medium", or "fast"
        // The time duration of the show animation
        showEffectSpeed: "medium",

        // **hideEffect**: Accepts String: "none", "fadeOut", "hide", or "slideUp"
        // Used to hide any of the table of contents nested items
        hideEffect: "slideUp",

        // **hideEffectSpeed**: Accepts Number (milliseconds) or String: "slow", "medium", or "fast"
        // The time duration of the hide animation
        hideEffectSpeed: "medium",

        // **smoothScroll**: Accepts a boolean: true or false
        // Determines if a jQuery animation should be used to scroll to specific table of contents items on the page
        smoothScroll: true,

        // **smoothScrollSpeed**: Accepts Number (milliseconds) or String: "slow", "medium", or "fast"
        // The time duration of the smoothScroll animation
        smoothScrollSpeed: "medium",

        // **scrollTo**: Accepts Number (pixels)
        // The amount of space between the top of page and the selected table of contents item after the page has been scrolled
        scrollTo: 0,

        // **showAndHideOnScroll**: Accepts a boolean: true or false
        // Determines if table of contents nested items should be shown and hidden while scrolling
        showAndHideOnScroll: true,

        // **highlightOnScroll**: Accepts a boolean: true or false
        // Determines if table of contents nested items should be highlighted (set to a different color) while scrolling
        highlightOnScroll: true,

        // **highlightOffset**: Accepts a number
        // The offset distance in pixels to trigger the next active table of contents item
        highlightOffset: 40,

        // **theme**: Accepts a string: "bootstrap", "jqueryui", or "none"
        // Determines if Twitter Bootstrap, jQueryUI, or Tocify classes should be added to the table of contents
        theme: "bootstrap",

        // **extendPage**: Accepts a boolean: true or false
        // If a user scrolls to the bottom of the page and the page is not tall enough to scroll to the last table of contents item, then the page height is increased
        extendPage: true,

        // **extendPageOffset**: Accepts a number: pixels
        // How close to the bottom of the page a user must scroll before the page is extended
        extendPageOffset: 100,

        // **history**: Accepts a boolean: true or false
        // Adds a hash to the page url to maintain history
        history: true,

        // **scrollHistory**: Accepts a boolean: true or false
        // Adds a hash to the page url, to maintain history, when scrolling to a TOC item
        scrollHistory: false,

        // **hashGenerator**: How the hash value (the anchor segment of the URL, following the
        // # character) will be generated.
        //
        // "compact" (default) - #CompressesEverythingTogether
        // "pretty" - #looks-like-a-nice-url-and-is-easily-readable
        // function(text, element){} - Your own hash generation function that accepts the text as an
        // argument, and returns the hash value.
        hashGenerator: "compact",

        // **highlightDefault**: Accepts a boolean: true or false
        // Set's the first TOC item as active if no other TOC item is active.
        highlightDefault: true

      },

      // _Create
      // -------
      //      Constructs the plugin.  Only called once.
      _create: function() {

        var self = this;

        self.extendPageScroll = true;

        // Internal array that keeps track of all TOC items (Helps to recognize if there are duplicate TOC item strings)
        self.items = [];

        // Generates the HTML for the dynamic table of contents
        self._generateToc();

        // Adds CSS classes to the newly generated table of contents HTML
        self._addCSSClasses();

        self.webkit = (function() {

          for (var prop in window) {

            if (prop) {

              if (prop.toLowerCase().indexOf("webkit") !== -1) {

                return true;

              }

            }

          }

          return false;

        }());

        // Adds jQuery event handlers to the newly generated table of contents
        self._setEventHandlers();

        // Binding to the Window load event to make sure the correct scrollTop is calculated
        $(window).on("load", function() {

          // Sets the active TOC item
          self._setActiveElement(true);

          // Once all animations on the page are complete, this callback function will be called
          $("html, body").promise().done(function() {

            setTimeout(function() {

              self.extendPageScroll = false;

            }, 0);

          });

        });

      },

      // _generateToc
      // ------------
      //      Generates the HTML for the dynamic table of contents
      _generateToc: function() {

        // _Local variables_

        // Stores the plugin context in the self variable
        var self = this,

          // All of the HTML tags found within the context provided (i.e. body) that match the top level jQuery selector above
          firstElem,

          // Instantiated variable that will store the top level newly created unordered list DOM element
          ul,
          ignoreSelector = self.options.ignoreSelector;


        // Determine the element to start the toc with
        // get all the top level selectors
        firstElem = [];
        var selectors = this.options.selectors.replace(/ /g, "").split(",");
        // find the first set that have at least one non-ignored element
        for(var i = 0; i < selectors.length; i++) {
          var foundSelectors = $(this.options.context).find(selectors[i]);
          for (var s = 0; s < foundSelectors.length; s++) {
            if (!$(foundSelectors[s]).is(ignoreSelector)) {
              firstElem = foundSelectors;
              break;
            }
          }
          if (firstElem.length> 0)
            break;
        }

        if (!firstElem.length) {

          self.element.addClass(hideTocClassName);

          return;

        }

        self.element.addClass(tocClassName);

        // Loops through each top level selector
        firstElem.each(function(index) {

          //If the element matches the ignoreSelector then we skip it
          if ($(this).is(ignoreSelector)) {
            return;
          }

          // Creates an unordered list HTML element and adds a dynamic ID and standard class name
          ul = $("<ul/>", {
            "id": headerClassName + index,
            "class": headerClassName
          }).

          // Appends a top level list item HTML element to the previously created HTML header
          append(self._nestElements($(this), index));

          // Add the created unordered list element to the HTML element calling the plugin
          self.element.append(ul);

          // Finds all of the HTML tags between the header and subheader elements
          $(this).nextUntil(this.nodeName.toLowerCase()).each(function() {

            // If there are no nested subheader elemements
            if ($(this).find(self.options.selectors).length === 0) {

              // Loops through all of the subheader elements
              $(this).filter(self.options.selectors).each(function() {

                //If the element matches the ignoreSelector then we skip it
                if ($(this).is(ignoreSelector)) {
                  return;
                }

                self._appendSubheaders.call(this, self, ul);

              });

            }

            // If there are nested subheader elements
            else {

              // Loops through all of the subheader elements
              $(this).find(self.options.selectors).each(function() {

                //If the element matches the ignoreSelector then we skip it
                if ($(this).is(ignoreSelector)) {
                  return;
                }

                self._appendSubheaders.call(this, self, ul);

              });

            }

          });

        });

      },

      _setActiveElement: function(pageload) {

        var self = this,

          hash = window.location.hash.substring(1),

          elem = self.element.find('li[data-unique="' + hash + '"]');

        if (hash.length) {

          // Removes highlighting from all of the list item's
          self.element.find("." + self.focusClass).removeClass(self.focusClass);

          // Highlights the current list item that was clicked
          elem.addClass(self.focusClass);

          // Triggers the click event on the currently focused TOC item
          elem.click();

        } else {

          // Removes highlighting from all of the list item's
          self.element.find("." + self.focusClass).removeClass(self.focusClass);

          if (!hash.length && pageload && self.options.highlightDefault) {

            // Highlights the first TOC item if no other items are highlighted
            self.element.find(itemClass).first().addClass(self.focusClass);

          }

        }

        return self;

      },

      // _nestElements
      // -------------
      //      Helps create the table of contents list by appending nested list items
      _nestElements: function(self, index) {

        var arr, item, hashValue;

        arr = $.grep(this.items, function(item) {

          return item === self.text();

        });

        // If there is already a duplicate TOC item
        if (arr.length) {

          // Adds the current TOC item text and index (for slight randomization) to the internal array
          this.items.push(self.text() + index);

        }

        // If there not a duplicate TOC item
        else {

          // Adds the current TOC item text to the internal array
          this.items.push(self.text());

        }

        hashValue = this._generateHashValue(arr, self, index);

        // Appends a list item HTML element to the last unordered list HTML element found within the HTML element calling the plugin
        item = $("<li/>", {

          // Sets a common class name to the list item
          "class": itemClassName,

          "data-unique": hashValue

        });

        if (this.options.theme !== "bootstrap3") {

          item.append($("<a/>", {

            "html": self.html()

          }));

        } else {

          item.html(self.html());

        }

        // Adds an HTML anchor tag before the currently traversed HTML element
        self.before($("<div/>", {

          // Sets a name attribute on the anchor tag to the text of the currently traversed HTML element (also making sure that all whitespace is replaced with an underscore)
          "name": hashValue,

          "data-unique": hashValue

        }));

        return item;

      },

      // _generateHashValue
      // ------------------
      //      Generates the hash value that will be used to refer to each item.
      _generateHashValue: function(arr, self, index) {

        var hashValue = "",
          hashGeneratorOption = this.options.hashGenerator;

        if (hashGeneratorOption === "pretty") {

          // prettify the text
          hashValue = self.text().toLowerCase().replace(/\s/g, "-");

          // fix double hyphens
          while (hashValue.indexOf("--") > -1) {
            hashValue = hashValue.replace(/--/g, "-");
          }

          // fix colon-space instances
          while (hashValue.indexOf(":-") > -1) {
            hashValue = hashValue.replace(/:-/g, "-");
          }

        } else if (typeof hashGeneratorOption === "function") {

          // call the function
          hashValue = hashGeneratorOption(self.text(), self);

        } else {

          // compact - the default
          hashValue = self.text().replace(/\s/g, "");

        }

        // add the index if we need to
        if (arr.length) {
          hashValue += "" + index;
        }

        // return the value
        return hashValue;

      },

      // _appendElements
      // ---------------
      //      Helps create the table of contents list by appending subheader elements

      _appendSubheaders: function(self, ul) {

        // The current element index
        var index = $(this).index(self.options.selectors),

          // Finds the previous header DOM element
          previousHeader = $(self.options.selectors).eq(index - 1),

          currentTagName = +$(this).prop("tagName").charAt(1),

          previousTagName = +previousHeader.prop("tagName").charAt(1),

          lastSubheader;

        // If the current header DOM element is smaller than the previous header DOM element or the first subheader
        if (currentTagName < previousTagName) {

          // Selects the last unordered list HTML found within the HTML element calling the plugin
          self.element.find(subheaderClass + "[data-tag=" + currentTagName + "]").last().append(self._nestElements($(this), index));

        }

        // If the current header DOM element is the same type of header(eg. h4) as the previous header DOM element
        else if (currentTagName === previousTagName) {

          ul.find(itemClass).last().after(self._nestElements($(this), index));

        } else {

          // Selects the last unordered list HTML found within the HTML element calling the plugin
          ul.find(itemClass).last().

          // Appends an unorderedList HTML element to the dynamic `unorderedList` variable and sets a common class name
          after($("<ul/>", {

            "class": subheaderClassName,

            "data-tag": currentTagName

          })).next(subheaderClass).

          // Appends a list item HTML element to the last unordered list HTML element found within the HTML element calling the plugin
          append(self._nestElements($(this), index));
        }

      },

      // _setEventHandlers
      // ----------------
      //      Adds jQuery event handlers to the newly generated table of contents
      _setEventHandlers: function() {

        // _Local variables_

        // Stores the plugin context in the self variable
        var self = this,

          // Instantiates a new variable that will be used to hold a specific element's context
          $self,

          // Instantiates a new variable that will be used to determine the smoothScroll animation time duration
          duration;

        // Event delegation that looks for any clicks on list item elements inside of the HTML element calling the plugin
        this.element.on("click.tocify", "li", function(event) {

          if (self.options.history) {

            window.location.hash = $(this).attr("data-unique");

          }

          // Removes highlighting from all of the list item's
          self.element.find("." + self.focusClass).removeClass(self.focusClass);

          // Highlights the current list item that was clicked
          $(this).addClass(self.focusClass);

          // If the showAndHide option is true
          if (self.options.showAndHide) {

            var elem = $('li[data-unique="' + $(this).attr("data-unique") + '"]');

            self._triggerShow(elem);

          }

          self._scrollTo($(this));

        });

        // Mouseenter and Mouseleave event handlers for the list item's within the HTML element calling the plugin
        this.element.find("li").on({

          // Mouseenter event handler
          "mouseenter.tocify": function() {

            // Adds a hover CSS class to the current list item
            $(this).addClass(self.hoverClass);

            // Makes sure the cursor is set to the pointer icon
            $(this).css("cursor", "pointer");

          },

          // Mouseleave event handler
          "mouseleave.tocify": function() {

            if (self.options.theme !== "bootstrap") {

              // Removes the hover CSS class from the current list item
              $(this).removeClass(self.hoverClass);

            }

          }
        });

        // only attach handler if needed (expensive in IE)
        if (self.options.extendPage || self.options.highlightOnScroll || self.options.scrollHistory || self.options.showAndHideOnScroll) {
          // Window scroll event handler
          $(window).on("scroll.tocify", function() {

            // Once all animations on the page are complete, this callback function will be called
            $("html, body").promise().done(function() {

              // Local variables

              // Stores how far the user has scrolled
              var winScrollTop = $(window).scrollTop(),

                // Stores the height of the window
                winHeight = $(window).height(),

                // Stores the height of the document
                docHeight = $(document).height(),

                scrollHeight = $("body")[0].scrollHeight,

                // Instantiates a variable that will be used to hold a selected HTML element
                elem,

                lastElem,

                lastElemOffset,

                currentElem;

              if (self.options.extendPage) {

                // If the user has scrolled to the bottom of the page and the last toc item is not focused
                if ((self.webkit && winScrollTop >= scrollHeight - winHeight - self.options.extendPageOffset) || (!self.webkit && winHeight + winScrollTop > docHeight - self.options.extendPageOffset)) {

                  if (!$(extendPageClass).length) {

                    lastElem = $('div[data-unique="' + $(itemClass).last().attr("data-unique") + '"]');

                    if (!lastElem.length) return;

                    // Gets the top offset of the page header that is linked to the last toc item
                    lastElemOffset = lastElem.offset().top;

                    // Appends a div to the bottom of the page and sets the height to the difference of the window scrollTop and the last element's position top offset
                    $(self.options.context).append($("<div/>", {

                      "class": extendPageClassName,

                      "height": Math.abs(lastElemOffset - winScrollTop) + "px",

                      "data-unique": extendPageClassName

                    }));

                    if (self.extendPageScroll) {

                      currentElem = self.element.find('li.' + self.focusClass);

                      self._scrollTo($('div[data-unique="' + currentElem.attr("data-unique") + '"]'));

                    }

                  }

                }

              }

              // The zero timeout ensures the following code is run after the scroll events
              setTimeout(function() {

                // _Local variables_

                // Stores the distance to the closest anchor
                var closestAnchorDistance = null,

                  // Stores the index of the closest anchor
                  closestAnchorIdx = null,

                  // Keeps a reference to all anchors
                  anchors = $(self.options.context).find("div[data-unique]"),

                  anchorText;

                // Determines the index of the closest anchor
                anchors.each(function(idx) {
                  var distance = Math.abs(($(this).next().length ? $(this).next() : $(this)).offset().top - winScrollTop - self.options.highlightOffset);
                  if (closestAnchorDistance == null || distance < closestAnchorDistance) {
                    closestAnchorDistance = distance;
                    closestAnchorIdx = idx;
                  } else {
                    return false;
                  }
                });

                anchorText = $(anchors[closestAnchorIdx]).attr("data-unique");

                // Stores the list item HTML element that corresponds to the currently traversed anchor tag
                elem = $('li[data-unique="' + anchorText + '"]');

                // If the `highlightOnScroll` option is true and a next element is found
                if (self.options.highlightOnScroll && elem.length) {

                  // Removes highlighting from all of the list item's
                  self.element.find("." + self.focusClass).removeClass(self.focusClass);

                  // Highlights the corresponding list item
                  elem.addClass(self.focusClass);

                }

                if (self.options.scrollHistory) {

                  if (window.location.hash !== "#" + anchorText) {

                    window.location.replace("#" + anchorText);

                  }
                }

                // If the `showAndHideOnScroll` option is true
                if (self.options.showAndHideOnScroll && self.options.showAndHide) {

                  self._triggerShow(elem, true);

                }

              }, 0);

            });

          });
        }

      },

      // Show
      // ----
      //      Opens the current sub-header
      show: function(elem, scroll) {

        // Stores the plugin context in the `self` variable
        var self = this,
          element = elem;

        // If the sub-header is not already visible
        if (!elem.is(":visible")) {

          // If the current element does not have any nested subheaders, is not a header, and its parent is not visible
          if (!elem.find(subheaderClass).length && !elem.parent().is(headerClass) && !elem.parent().is(":visible")) {

            // Sets the current element to all of the subheaders within the current header
            elem = elem.parents(subheaderClass).add(elem);

          }

          // If the current element does not have any nested subheaders and is not a header
          else if (!elem.children(subheaderClass).length && !elem.parent().is(headerClass)) {

            // Sets the current element to the closest subheader
            elem = elem.closest(subheaderClass);

          }

          //Determines what jQuery effect to use
          switch (self.options.showEffect) {

            //Uses `no effect`
            case "none":

              elem.show();

              break;

              //Uses the jQuery `show` special effect
            case "show":

              elem.show(self.options.showEffectSpeed);

              break;

              //Uses the jQuery `slideDown` special effect
            case "slideDown":

              elem.slideDown(self.options.showEffectSpeed);

              break;

              //Uses the jQuery `fadeIn` special effect
            case "fadeIn":

              elem.fadeIn(self.options.showEffectSpeed);

              break;

              //If none of the above options were passed, then a `jQueryUI show effect` is expected
            default:

              elem.show();

              break;

          }

        }

        // If the current subheader parent element is a header
        if (elem.parent().is(headerClass)) {

          // Hides all non-active sub-headers
          self.hide($(subheaderClass).not(elem));

        }

        // If the current subheader parent element is not a header
        else {

          // Hides all non-active sub-headers
          self.hide($(subheaderClass).not(elem.closest(headerClass).find(subheaderClass).not(elem.siblings())));

        }

        // Maintains chainablity
        return self;

      },

      // Hide
      // ----
      //      Closes the current sub-header
      hide: function(elem) {

        // Stores the plugin context in the `self` variable
        var self = this;

        //Determines what jQuery effect to use
        switch (self.options.hideEffect) {

          // Uses `no effect`
          case "none":

            elem.hide();

            break;

            // Uses the jQuery `hide` special effect
          case "hide":

            elem.hide(self.options.hideEffectSpeed);

            break;

            // Uses the jQuery `slideUp` special effect
          case "slideUp":

            elem.slideUp(self.options.hideEffectSpeed);

            break;

            // Uses the jQuery `fadeOut` special effect
          case "fadeOut":

            elem.fadeOut(self.options.hideEffectSpeed);

            break;

            // If none of the above options were passed, then a `jqueryUI hide effect` is expected
          default:

            elem.hide();

            break;

        }

        // Maintains chainablity
        return self;
      },

      // _triggerShow
      // ------------
      //      Determines what elements get shown on scroll and click
      _triggerShow: function(elem, scroll) {

        var self = this;

        // If the current element's parent is a header element or the next element is a nested subheader element
        if (elem.parent().is(headerClass) || elem.next().is(subheaderClass)) {

          // Shows the next sub-header element
          self.show(elem.next(subheaderClass), scroll);

        }

        // If the current element's parent is a subheader element
        else if (elem.parent().is(subheaderClass)) {

          // Shows the parent sub-header element
          self.show(elem.parent(), scroll);

        }

        // Maintains chainability
        return self;

      },

      // _addCSSClasses
      // --------------
      //      Adds CSS classes to the newly generated table of contents HTML
      _addCSSClasses: function() {

        // If the user wants a jqueryUI theme
        if (this.options.theme === "jqueryui") {

          this.focusClass = "ui-state-default";

          this.hoverClass = "ui-state-hover";

          //Adds the default styling to the dropdown list
          this.element.addClass("ui-widget").find(".toc-title").addClass("ui-widget-header").end().find("li").addClass("ui-widget-content");

        }

        // If the user wants a twitterBootstrap theme
        else if (this.options.theme === "bootstrap") {

          this.element.find(headerClass + "," + subheaderClass).addClass("nav nav-list");

          this.focusClass = "active";

        }

        // If the user wants a twitterBootstrap theme
        else if (this.options.theme === "bootstrap3") {

          this.element.find(headerClass + "," + subheaderClass).addClass("list-group");

          this.element.find(itemClass).addClass("list-group-item");

          this.focusClass = "active";

        }

        // If a user does not want a prebuilt theme
        else {

          // Adds more neutral classes (instead of jqueryui)

          this.focusClass = tocFocusClassName;

          this.hoverClass = tocHoverClassName;

        }

        //Maintains chainability
        return this;

      },

      // setOption
      // ---------
      //      Sets a single Tocify option after the plugin is invoked
      setOption: function() {

        // Calls the jQueryUI Widget Factory setOption method
        $.Widget.prototype._setOption.apply(this, arguments);

      },

      // setOptions
      // ----------
      //      Sets a single or multiple Tocify options after the plugin is invoked
      setOptions: function() {

        // Calls the jQueryUI Widget Factory setOptions method
        $.Widget.prototype._setOptions.apply(this, arguments);

      },

      // _scrollTo
      // ---------
      //      Scrolls to a specific element
      _scrollTo: function(elem) {

        var self = this,
          duration = self.options.smoothScroll || 0,
          scrollTo = self.options.scrollTo,
          currentDiv = $('div[data-unique="' + elem.attr("data-unique") + '"]');

        if (!currentDiv.length) {

          return self;

        }

        // Once all animations on the page are complete, this callback function will be called
        $("html, body").promise().done(function() {

          // Animates the html and body element scrolltops
          $("html, body").animate({

            // Sets the jQuery `scrollTop` to the top offset of the HTML div tag that matches the current list item's `data-unique` tag
            "scrollTop": currentDiv.offset().top - ($.isFunction(scrollTo) ? scrollTo.call() : scrollTo) + "px"

          }, {

            // Sets the smoothScroll animation time duration to the smoothScrollSpeed option
            "duration": duration

          });

        });

        // Maintains chainability
        return self;

      }

    });

  })); //end of plugin
</script>
<script>

/**
 * jQuery Plugin: Sticky Tabs
 *
 * @author Aidan Lister <aidan@php.net>
 * adapted by Ruben Arslan to activate parent tabs too
 * http://www.aidanlister.com/2014/03/persisting-the-tab-state-in-bootstrap/
 */
(function($) {
  "use strict";
  $.fn.rmarkdownStickyTabs = function() {
    var context = this;
    // Show the tab corresponding with the hash in the URL, or the first tab
    var showStuffFromHash = function() {
      var hash = window.location.hash;
      var selector = hash ? 'a[href="' + hash + '"]' : 'li.active > a';
      var $selector = $(selector, context);
      if($selector.data('toggle') === "tab") {
        $selector.tab('show');
        // walk up the ancestors of this element, show any hidden tabs
        $selector.parents('.section.tabset').each(function(i, elm) {
          var link = $('a[href="#' + $(elm).attr('id') + '"]');
          if(link.data('toggle') === "tab") {
            link.tab("show");
          }
        });
      }
    };


    // Set the correct tab when the page loads
    showStuffFromHash(context);

    // Set the correct tab when a user uses their back/forward button
    $(window).on('hashchange', function() {
      showStuffFromHash(context);
    });

    // Change the URL when tabs are clicked
    $('a', context).on('click', function(e) {
      history.pushState(null, null, this.href);
      showStuffFromHash(context);
    });

    return this;
  };
}(jQuery));

window.buildTabsets = function(tocID) {

  // build a tabset from a section div with the .tabset class
  function buildTabset(tabset) {

    // check for fade and pills options
    var fade = tabset.hasClass("tabset-fade");
    var pills = tabset.hasClass("tabset-pills");
    var navClass = pills ? "nav-pills" : "nav-tabs";

    // determine the heading level of the tabset and tabs
    var match = tabset.attr('class').match(/level(\d) /);
    if (match === null)
      return;
    var tabsetLevel = Number(match[1]);
    var tabLevel = tabsetLevel + 1;

    // find all subheadings immediately below
    var tabs = tabset.find("div.section.level" + tabLevel);
    if (!tabs.length)
      return;

    // create tablist and tab-content elements
    var tabList = $('<ul class="nav ' + navClass + '" role="tablist"></ul>');
    $(tabs[0]).before(tabList);
    var tabContent = $('<div class="tab-content"></div>');
    $(tabs[0]).before(tabContent);

    // build the tabset
    var activeTab = 0;
    tabs.each(function(i) {

      // get the tab div
      var tab = $(tabs[i]);

      // get the id then sanitize it for use with bootstrap tabs
      var id = tab.attr('id');

      // see if this is marked as the active tab
      if (tab.hasClass('active'))
        activeTab = i;

      // remove any table of contents entries associated with
      // this ID (since we'll be removing the heading element)
      $("div#" + tocID + " li a[href='#" + id + "']").parent().remove();

      // sanitize the id for use with bootstrap tabs
      id = id.replace(/[.\/?&!#<>]/g, '').replace(/\s/g, '_');
      tab.attr('id', id);

      // get the heading element within it, grab it's text, then remove it
      var heading = tab.find('h' + tabLevel + ':first');
      var headingText = heading.html();
      heading.remove();

      // build and append the tab list item
      var a = $('<a role="tab" data-toggle="tab">' + headingText + '</a>');
      a.attr('href', '#' + id);
      a.attr('aria-controls', id);
      var li = $('<li role="presentation"></li>');
      li.append(a);
      tabList.append(li);

      // set it's attributes
      tab.attr('role', 'tabpanel');
      tab.addClass('tab-pane');
      tab.addClass('tabbed-pane');
      if (fade)
        tab.addClass('fade');

      // move it into the tab content div
      tab.detach().appendTo(tabContent);
    });

    // set active tab
    $(tabList.children('li')[activeTab]).addClass('active');
    var active = $(tabContent.children('div.section')[activeTab]);
    active.addClass('active');
    if (fade)
      active.addClass('in');

    if (tabset.hasClass("tabset-sticky"))
      tabset.rmarkdownStickyTabs();
  }

  // convert section divs with the .tabset class to tabsets
  var tabsets = $("div.section.tabset");
  tabsets.each(function(i) {
    buildTabset($(tabsets[i]));
  });
};

</script>
<style type="text/css">.pagedtable {
overflow: auto;
padding-left: 8px;
padding-right: 8px;
}
.pagedtable-wrapper {
border: 1px solid #ccc;
border-radius: 4px;
margin-bottom: 10px;
}
.pagedtable table {
width: 100%;
max-width: 100%;
margin: 0;
}
.pagedtable th {
padding: 0 5px 0 5px;
border: none;
border-bottom: 2px solid #dddddd;
min-width: 45px;
}
.pagedtable-empty th {
display: none;
}
.pagedtable td {
padding: 0 4px 0 4px;
}
.pagedtable .even {
background-color: rgba(140, 140, 140, 0.1);
}
.pagedtable-padding-col {
display: none;
}
.pagedtable a {
-webkit-touch-callout: none;
-webkit-user-select: none;
-khtml-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.pagedtable-index-nav {
cursor: pointer;
padding: 0 5px 0 5px;
float: right;
border: 0;
}
.pagedtable-index-nav-disabled {
cursor: default;
text-decoration: none;
color: #999;
}
a.pagedtable-index-nav-disabled:hover {
text-decoration: none;
color: #999;
}
.pagedtable-indexes {
cursor: pointer;
float: right;
border: 0;
}
.pagedtable-index-current {
cursor: default;
text-decoration: none;
font-weight: bold;
color: #333;
border: 0;
}
a.pagedtable-index-current:hover {
text-decoration: none;
font-weight: bold;
color: #333;
}
.pagedtable-index {
width: 30px;
display: inline-block;
text-align: center;
border: 0;
}
.pagedtable-index-separator-left {
display: inline-block;
color: #333;
font-size: 9px;
padding: 0 0 0 0;
cursor: default;
}
.pagedtable-index-separator-right {
display: inline-block;
color: #333;
font-size: 9px;
padding: 0 4px 0 0;
cursor: default;
}
.pagedtable-footer {
padding-top: 4px;
padding-bottom: 5px;
}
.pagedtable-not-empty .pagedtable-footer {
border-top: 2px solid #dddddd;
}
.pagedtable-info {
overflow: hidden;
color: #999;
white-space: nowrap;
text-overflow: ellipsis;
}
.pagedtable-header-name {
overflow: hidden;
text-overflow: ellipsis;
}
.pagedtable-header-type {
color: #999;
font-weight: 400;
}
.pagedtable-na-cell {
font-style: italic;
opacity: 0.3;
}
</style>
<script>// Production steps of ECMA-262, Edition 5, 15.4.4.18
// Reference: http://es5.github.io/#x15.4.4.18
if (!Array.prototype.forEach) {

  Array.prototype.forEach = function(callback, thisArg) {

    var T, k;

    if (this === null) {
      throw new TypeError(' this is null or not defined');
    }

    // 1. Let O be the result of calling toObject() passing the
    // |this| value as the argument.
    var O = Object(this);

    // 2. Let lenValue be the result of calling the Get() internal
    // method of O with the argument "length".
    // 3. Let len be toUint32(lenValue).
    var len = O.length >>> 0;

    // 4. If isCallable(callback) is false, throw a TypeError exception.
    // See: http://es5.github.com/#x9.11
    if (typeof callback !== "function") {
      throw new TypeError(callback + ' is not a function');
    }

    // 5. If thisArg was supplied, let T be thisArg; else let
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    if (arguments.length > 1) {
      T = thisArg;
    }

    // 6. Let k be 0
    k = 0;

    // 7. Repeat, while k < len
    while (k < len) {

      var kValue;

      // a. Let Pk be ToString(k).
      //    This is implicit for LHS operands of the in operator
      // b. Let kPresent be the result of calling the HasProperty
      //    internal method of O with argument Pk.
      //    This step can be combined with c
      // c. If kPresent is true, then
      if (k in O) {

        // i. Let kValue be the result of calling the Get internal
        // method of O with argument Pk.
        kValue = O[k];

        // ii. Call the Call internal method of callback with T as
        // the this value and argument list containing kValue, k, and O.
        callback.call(T, kValue, k, O);
      }
      // d. Increase k by 1.
      k++;
    }
    // 8. return undefined
  };
}

// Production steps of ECMA-262, Edition 5, 15.4.4.19
// Reference: http://es5.github.io/#x15.4.4.19
if (!Array.prototype.map) {

  Array.prototype.map = function(callback, thisArg) {

    var T, A, k;

    if (this == null) {
      throw new TypeError(' this is null or not defined');
    }

    // 1. Let O be the result of calling ToObject passing the |this|
    //    value as the argument.
    var O = Object(this);

    // 2. Let lenValue be the result of calling the Get internal
    //    method of O with the argument "length".
    // 3. Let len be ToUint32(lenValue).
    var len = O.length >>> 0;

    // 4. If IsCallable(callback) is false, throw a TypeError exception.
    // See: http://es5.github.com/#x9.11
    if (typeof callback !== 'function') {
      throw new TypeError(callback + ' is not a function');
    }

    // 5. If thisArg was supplied, let T be thisArg; else let T be undefined.
    if (arguments.length > 1) {
      T = thisArg;
    }

    // 6. Let A be a new array created as if by the expression new Array(len)
    //    where Array is the standard built-in constructor with that name and
    //    len is the value of len.
    A = new Array(len);

    // 7. Let k be 0
    k = 0;

    // 8. Repeat, while k < len
    while (k < len) {

      var kValue, mappedValue;

      // a. Let Pk be ToString(k).
      //   This is implicit for LHS operands of the in operator
      // b. Let kPresent be the result of calling the HasProperty internal
      //    method of O with argument Pk.
      //   This step can be combined with c
      // c. If kPresent is true, then
      if (k in O) {

        // i. Let kValue be the result of calling the Get internal
        //    method of O with argument Pk.
        kValue = O[k];

        // ii. Let mappedValue be the result of calling the Call internal
        //     method of callback with T as the this value and argument
        //     list containing kValue, k, and O.
        mappedValue = callback.call(T, kValue, k, O);

        // iii. Call the DefineOwnProperty internal method of A with arguments
        // Pk, Property Descriptor
        // { Value: mappedValue,
        //   Writable: true,
        //   Enumerable: true,
        //   Configurable: true },
        // and false.

        // In browsers that support Object.defineProperty, use the following:
        // Object.defineProperty(A, k, {
        //   value: mappedValue,
        //   writable: true,
        //   enumerable: true,
        //   configurable: true
        // });

        // For best browser support, use the following:
        A[k] = mappedValue;
      }
      // d. Increase k by 1.
      k++;
    }

    // 9. return A
    return A;
  };
}

var PagedTable = function (pagedTable) {
  var me = this;

  var source = function(pagedTable) {
    var sourceElems = [].slice.call(pagedTable.children).filter(function(e) {
      return e.hasAttribute("data-pagedtable-source");
    });

    if (sourceElems === null || sourceElems.length !== 1) {
      throw("A single data-pagedtable-source was not found");
    }

    return JSON.parse(sourceElems[0].innerHTML);
  }(pagedTable);

  var options = function(source) {
    var options = typeof(source.options) !== "undefined" &&
      source.options !== null ? source.options : {};

    var columns = typeof(options.columns) !== "undefined" ? options.columns : {};
    var rows = typeof(options.rows) !== "undefined" ? options.rows : {};

    var positiveIntOrNull = function(value) {
      return parseInt(value) >= 0 ? parseInt(value) : null;
    };

    return {
      pages: positiveIntOrNull(options.pages),
      rows: {
        min: positiveIntOrNull(rows.min),
        max: positiveIntOrNull(rows.max),
        total: positiveIntOrNull(rows.total)
      },
      columns: {
        min: positiveIntOrNull(columns.min),
        max: positiveIntOrNull(columns.max),
        total: positiveIntOrNull(columns.total)
      }
    };
  }(source);

  var Measurer = function() {

    // set some default initial values that will get adjusted in runtime
    me.measures = {
      padding: 12,
      character: 8,
      height: 15,
      defaults: true
    };

    me.calculate = function(measuresCell) {
      if (!me.measures.defaults)
        return;

      var measuresCellStyle = window.getComputedStyle(measuresCell, null);

      var newPadding = parsePadding(measuresCellStyle.paddingLeft) +
            parsePadding(measuresCellStyle.paddingRight);

      var sampleString = "ABCDEFGHIJ0123456789";
      var newCharacter = Math.ceil(measuresCell.clientWidth / sampleString.length);

      if (newPadding <= 0 || newCharacter <= 0)
        return;

      me.measures.padding = newPadding;
      me.measures.character = newCharacter;
      me.measures.height = measuresCell.clientHeight;
      me.measures.defaults = false;
    };

    return me;
  };

  var Page = function(data, options) {
    var me = this;

    var defaults = {
      max: 7,
      rows: 10
    };

    var totalPages = function() {
      return Math.ceil(data.length / me.rows);
    };

    me.number = 0;
    me.max = options.pages !== null ? options.pages : defaults.max;
    me.visible = me.max;
    me.rows = options.rows.min !== null ? options.rows.min : defaults.rows;
    me.total = totalPages();

    me.setRows = function(newRows) {
      me.rows = newRows;
      me.total = totalPages();
    };

    me.setPageNumber = function(newPageNumber) {
      if (newPageNumber < 0) newPageNumber = 0;
      if (newPageNumber >= me.total) newPageNumber = me.total - 1;

      me.number = newPageNumber;
    };

    me.setVisiblePages = function(visiblePages) {
      me.visible = Math.min(me.max, visiblePages);
      me.setPageNumber(me.number);
    };

    me.getVisiblePageRange = function() {
      var start = me.number - Math.max(Math.floor((me.visible - 1) / 2), 0);
      var end = me.number + Math.floor(me.visible / 2) + 1;
      var pageCount = me.total;

      if (start < 0) {
        var diffToStart = 0 - start;
        start += diffToStart;
        end += diffToStart;
      }

      if (end > pageCount) {
        var diffToEnd = end - pageCount;
        start -= diffToEnd;
        end -= diffToEnd;
      }

      start = start < 0 ? 0 : start;
      end = end >= pageCount ? pageCount : end;

      var first = false;
      var last = false;

      if (start > 0 && me.visible > 1) {
        start = start + 1;
        first = true;
      }

      if (end < pageCount && me.visible > 2) {
        end = end - 1;
        last = true;
      }

      return {
        first: first,
        start: start,
        end: end,
        last: last
      };
    };

    me.getRowStart = function() {
      var rowStart = page.number * page.rows;
      if (rowStart < 0)
        rowStart = 0;

      return rowStart;
    };

    me.getRowEnd = function() {
      var rowStart = me.getRowStart();
      return Math.min(rowStart + me.rows, data.length);
    };

    me.getPaddingRows = function() {
      var rowStart = me.getRowStart();
      var rowEnd = me.getRowEnd();
      return data.length > me.rows ? me.rows - (rowEnd - rowStart) : 0;
    };
  };

  var Columns = function(data, columns, options) {
    var me = this;

    me.defaults = {
      min: 5
    };

    me.number = 0;
    me.visible = 0;
    me.total = columns.length;
    me.subset = [];
    me.padding = 0;
    me.min = options.columns.min !== null ? options.columns.min : me.defaults.min;
    me.max = options.columns.max !== null ? options.columns.max : null;
    me.widths = {};

    var widthsLookAhead = Math.max(100, options.rows.min);
    var paddingColChars = 10;

    me.emptyNames = function() {
      columns.forEach(function(column) {
        if (columns.label !== null && columns.label !== "")
          return false;
      });

      return true;
    };

    var parsePadding = function(value) {
      return parseInt(value) >= 0 ? parseInt(value) : 0;
    };

    me.calculateWidths = function(measures) {
      columns.forEach(function(column) {
        var maxChars = Math.max(
          column.label.toString().length,
          column.type.toString().length
        );

        for (var idxRow = 0; idxRow < Math.min(widthsLookAhead, data.length); idxRow++) {
          maxChars = Math.max(maxChars, data[idxRow][column.name.toString()].length);
        }

        me.widths[column.name] = {
          // width in characters
          chars: maxChars,
          // width for the inner html columns
          inner: maxChars * measures.character,
          // width adding outer styles like padding
          outer: maxChars * measures.character + measures.padding
        };
      });
    };

    me.getWidth = function() {
      var widthOuter = 0;
      for (var idxCol = 0; idxCol < me.subset.length; idxCol++) {
        var columnName = me.subset[idxCol].name;
        widthOuter = widthOuter + me.widths[columnName].outer;
      }

      widthOuter = widthOuter + me.padding * paddingColChars * measurer.measures.character;

      if (me.hasMoreLeftColumns()) {
        widthOuter = widthOuter + columnNavigationWidthPX + measurer.measures.padding;
      }

      if (me.hasMoreRightColumns()) {
        widthOuter = widthOuter + columnNavigationWidthPX + measurer.measures.padding;
      }

      return widthOuter;
    };

    me.updateSlice = function() {
      if (me.number + me.visible >= me.total)
        me.number = me.total - me.visible;

      if (me.number < 0) me.number = 0;

      me.subset = columns.slice(me.number, Math.min(me.number + me.visible, me.total));

      me.subset = me.subset.map(function(column) {
        Object.keys(column).forEach(function(colKey) {
          column[colKey] = column[colKey] === null ? "" : column[colKey].toString();
        });

        column.width = null;
        return column;
      });
    };

    me.setVisibleColumns = function(columnNumber, newVisibleColumns, paddingCount) {
      me.number = columnNumber;
      me.visible = newVisibleColumns;
      me.padding = paddingCount;

      me.updateSlice();
    };

    me.incColumnNumber = function(increment) {
      me.number = me.number + increment;
    };

    me.setColumnNumber = function(newNumber) {
      me.number = newNumber;
    };

    me.setPaddingCount = function(newPadding) {
      me.padding = newPadding;
    };

    me.getPaddingCount = function() {
      return me.padding;
    };

    me.hasMoreLeftColumns = function() {
      return me.number > 0;
    };

    me.hasMoreRightColumns = function() {
      return me.number + me.visible < me.total;
    };

    me.updateSlice(0);
    return me;
  };

  var data = source.data;
  var page = new Page(data, options);
  var measurer = new Measurer(data, options);
  var columns = new Columns(data, source.columns, options);

  var table = null;
  var tableDiv = null;
  var header = null;
  var footer = null;
  var tbody = null;

  // Caches pagedTable.clientWidth, specially for webkit
  var cachedPagedTableClientWidth = null;

  var onChangeCallbacks = [];

  var clearSelection = function() {
    if(document.selection && document.selection.empty) {
      document.selection.empty();
    } else if(window.getSelection) {
      var sel = window.getSelection();
      sel.removeAllRanges();
    }
  };

  var columnNavigationWidthPX = 5;

  var renderColumnNavigation = function(increment, backwards) {
    var arrow = document.createElement("div");
    arrow.setAttribute("style",
      "border-top: " + columnNavigationWidthPX + "px solid transparent;" +
      "border-bottom: " + columnNavigationWidthPX + "px solid transparent;" +
      "border-" + (backwards ? "right" : "left") + ": " + columnNavigationWidthPX + "px solid;");

    var header = document.createElement("th");
    header.appendChild(arrow);
    header.setAttribute("style",
      "cursor: pointer;" +
      "vertical-align: middle;" +
      "min-width: " + columnNavigationWidthPX + "px;" +
      "width: " + columnNavigationWidthPX + "px;");

    header.onclick = function() {
      columns.incColumnNumber(backwards ? -1 : increment);

      me.animateColumns(backwards);
      renderFooter();

      clearSelection();
      triggerOnChange();
    };

    return header;
  };

  var maxColumnWidth = function(width) {
    var padding = 80;
    var columnMax = Math.max(cachedPagedTableClientWidth - padding, 0);

    return parseInt(width) > 0 ?
      Math.min(columnMax, parseInt(width)) + "px" :
      columnMax + "px";
  };

  var clearHeader = function() {
    var thead = pagedTable.querySelectorAll("thead")[0];
    thead.innerHTML = "";
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  var renderHeader = function(clear) {
    cachedPagedTableClientWidth = pagedTable.clientWidth;

    var fragment = document.createDocumentFragment();

    header = document.createElement("tr");
    fragment.appendChild(header);

    if (columns.number > 0)
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    columns.subset = columns.subset.map(function(columnData) {
      var column = document.createElement("th");
      column.setAttribute("align", columnData.align);
      column.style.textAlign = columnData.align;

      column.style.maxWidth = maxColumnWidth(null);
      if (columnData.width) {
        column.style.minWidth =
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      }

      var columnName = document.createElement("div");
      columnName.setAttribute("class", "pagedtable-header-name");
      if (columnData.label === "") {
        columnName.innerHTML = "&nbsp;";
      }
      else {
        columnName.appendChild(document.createTextNode(columnData.label));
      }
      column.appendChild(columnName);

      var columnType = document.createElement("div");
      columnType.setAttribute("class", "pagedtable-header-type");
      if (columnData.type === "") {
        columnType.innerHTML = "&nbsp;";
      }
      else {
        columnType.appendChild(document.createTextNode("<" + columnData.type + ">"));
      }
      column.appendChild(columnType);

      header.appendChild(column);

      columnData.element = column;

      return columnData;
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    for (var idx = 0; idx < columns.getPaddingCount(); idx++) {
      var paddingCol = document.createElement("th");
      paddingCol.setAttribute("class", "pagedtable-padding-col");
      header.appendChild(paddingCol);
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    if (columns.number + columns.visible < columns.total)
      header.appendChild(renderColumnNavigation(columns.visible, false));

    if (typeof(clear) == "undefined" || clear) clearHeader();
    var thead = pagedTable.querySelectorAll("thead")[0];
    thead.appendChild(fragment);
  };

  me.animateColumns = function(backwards) {
    var thead = pagedTable.querySelectorAll("thead")[0];

    var headerOld = thead.querySelectorAll("tr")[0];
    var tbodyOld = table.querySelectorAll("tbody")[0];

    me.fitColumns(backwards);

    renderHeader(false);

    header.style.opacity = "0";
    header.style.transform = backwards ? "translateX(-30px)" : "translateX(30px)";
    header.style.transition = "transform 200ms linear, opacity 200ms";
    header.style.transitionDelay = "0";

    renderBody(false);

    if (headerOld) {
      headerOld.style.position = "absolute";
      headerOld.style.transform = "translateX(0px)";
      headerOld.style.opacity = "1";
      headerOld.style.transition = "transform 100ms linear, opacity 100ms";
      headerOld.setAttribute("class", "pagedtable-remove-head");
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<h1 class="title toc-ignore">Public Responses to Engineering Equality:
Gender Quotas and Satisfaction with Democracy: Supplementary Replication
Materials</h1>
<h4 class="author">Neil Williams &amp; Alexandra Snipes</h4>
<h4 class="date">June 2024</h4>

</div>


<p>This is the document detailing the replication for models,
postestimation, figures, and tables that appear in both the text and
appendix for <strong><em>Public Responses to Engineering Equality:
Gender Quotas and Satisfaction with Democracy</em></strong></p>
<div id="study-1" class="section level1">
<h1>Study 1</h1>
<div id="load-needed-packages-and-functions" class="section level2">
<h2>Load Needed packages and functions</h2>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">library</span>(lme4)</span></code></pre></div>
<pre><code>## Loading required package: Matrix</code></pre>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a><span class="fu">library</span>(emmeans)</span>
<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a><span class="fu">library</span>(sjPlot)</span></code></pre></div>
<pre><code>## Install package &quot;strengejacke&quot; from GitHub (`devtools::install_github(&quot;strengejacke/strengejacke&quot;)`) to load all sj-packages at once!</code></pre>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="fu">library</span>(jtools)</span>
<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a><span class="fu">library</span>(ggpubr)</span>
<span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a><span class="fu">library</span>(dplyr)</span></code></pre></div>
<pre><code>## 
## Attaching package: &#39;dplyr&#39;</code></pre>
<pre><code>## The following objects are masked from &#39;package:stats&#39;:
## 
##     filter, lag</code></pre>
<pre><code>## The following objects are masked from &#39;package:base&#39;:
## 
##     intersect, setdiff, setequal, union</code></pre>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a><span class="fu">library</span>(haven)</span>
<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a><span class="fu">library</span>(broom.mixed)</span>
<span id="cb9-3"><a href="#cb9-3" tabindex="-1"></a><span class="fu">library</span>(insight)</span></code></pre></div>
<pre><code>## 
## Attaching package: &#39;insight&#39;</code></pre>
<pre><code>## The following objects are masked from &#39;package:jtools&#39;:
## 
##     get_data, get_weights</code></pre>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" tabindex="-1"></a><span class="fu">library</span>(stargazer)</span></code></pre></div>
<pre><code>## 
## Please cite as:</code></pre>
<pre><code>##  Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.</code></pre>
<pre><code>##  R package version 5.2.3. https://CRAN.R-project.org/package=stargazer</code></pre>
</div>
<div id="model-estimation" class="section level2">
<h2>Model Estimation</h2>
<p>Estimate models for <strong><em>Study 1</em></strong></p>
<div id="americasbarometer" class="section level3">
<h3>AmericasBarometer</h3>
<p>Estimate AmericasBarometer models for <strong><em>Study
1</em></strong></p>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" tabindex="-1"></a>Americas_analysis <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Analysis Data/Americas_analysis.rds&quot;</span>)</span></code></pre></div>
<div id="hyothesis-1a-and-1b" class="section level4">
<h4>Hyothesis 1a and 1b</h4>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" tabindex="-1"></a>Americas_hyp1 <span class="ot">&lt;-</span> <span class="fu">lmer</span>(Satisfaction <span class="sc">~</span> quota_effect <span class="sc">+</span></span>
<span id="cb17-2"><a href="#cb17-2" tabindex="-1"></a>                     Female <span class="sc">+</span> </span>
<span id="cb17-3"><a href="#cb17-3" tabindex="-1"></a>                     Ideology <span class="sc">+</span> </span>
<span id="cb17-4"><a href="#cb17-4" tabindex="-1"></a>                    Education <span class="sc">+</span> </span>
<span id="cb17-5"><a href="#cb17-5" tabindex="-1"></a>                    Income <span class="sc">+</span></span>
<span id="cb17-6"><a href="#cb17-6" tabindex="-1"></a>                    Age <span class="sc">+</span> </span>
<span id="cb17-7"><a href="#cb17-7" tabindex="-1"></a>                    Polity_score <span class="sc">+</span> </span>
<span id="cb17-8"><a href="#cb17-8" tabindex="-1"></a>                    GDP_capita_logged <span class="sc">+</span> </span>
<span id="cb17-9"><a href="#cb17-9" tabindex="-1"></a>                    women_rep_lag <span class="sc">+</span> </span>
<span id="cb17-10"><a href="#cb17-10" tabindex="-1"></a>                    Corruption <span class="sc">+</span></span>
<span id="cb17-11"><a href="#cb17-11" tabindex="-1"></a>                    Presidentialism <span class="sc">+</span></span>
<span id="cb17-12"><a href="#cb17-12" tabindex="-1"></a>                    (<span class="dv">1</span> <span class="sc">|</span> Country_year), <span class="at">data =</span> Americas_analysis)</span></code></pre></div>
<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" tabindex="-1"></a><span class="fu">summary</span>(Americas_hyp1)</span></code></pre></div>
<pre><code>## Linear mixed model fit by REML [&#39;lmerMod&#39;]
## Formula: Satisfaction ~ quota_effect + Female + Ideology + Education +  
##     Income + Age + Polity_score + GDP_capita_logged + women_rep_lag +  
##     Corruption + Presidentialism + (1 | Country_year)
##    Data: Americas_analysis
## 
## REML criterion at convergence: -12209.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2061 -0.7418  0.2637  0.6848  3.0979 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Country_year (Intercept) 0.002811 0.05302 
##  Residual                 0.052029 0.22810 
## Number of obs: 107527, groups:  Country_year, 101
## 
## Fixed effects:
##                            Estimate Std. Error t value
## (Intercept)                0.571009   0.061697   9.255
## quota_effectNon-Effective  0.029491   0.015247   1.934
## quota_effectEffective     -0.015634   0.016368  -0.955
## FemaleWoman               -0.006983   0.001403  -4.977
## Ideology                   0.018582   0.002527   7.353
## Education                 -0.057381   0.003393 -16.911
## Income                     0.003150   0.004508   0.699
## Age                        0.010400   0.004119   2.525
## Polity_score              -0.018618   0.049211  -0.378
## GDP_capita_logged          0.054807   0.042153   1.300
## women_rep_lag              0.036727   0.039576   0.928
## Corruption                -0.147765   0.034027  -4.343
## Presidentialism            0.069061   0.035375   1.952</code></pre>
<pre><code>## 
## Correlation matrix not shown by default, as p = 13 &gt; 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it</code></pre>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" tabindex="-1"></a><span class="fu">saveRDS</span>(Americas_hyp1, <span class="st">&quot;Americas_hyp1.rds&quot;</span>)</span></code></pre></div>
</div>
<div id="generation-robustness" class="section level4">
<h4>Generation Robustness</h4>
<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" tabindex="-1"></a>Americas_hyp1_generation <span class="ot">&lt;-</span> <span class="fu">lmer</span>(Satisfaction <span class="sc">~</span> quota_effect <span class="sc">+</span></span>
<span id="cb22-2"><a href="#cb22-2" tabindex="-1"></a>                     Female <span class="sc">+</span> </span>
<span id="cb22-3"><a href="#cb22-3" tabindex="-1"></a>                     Ideology <span class="sc">+</span> </span>
<span id="cb22-4"><a href="#cb22-4" tabindex="-1"></a>                    Education <span class="sc">+</span> </span>
<span id="cb22-5"><a href="#cb22-5" tabindex="-1"></a>                    Income <span class="sc">+</span></span>
<span id="cb22-6"><a href="#cb22-6" tabindex="-1"></a>                    Age <span class="sc">+</span> </span>
<span id="cb22-7"><a href="#cb22-7" tabindex="-1"></a>                    Generation <span class="sc">+</span> </span>
<span id="cb22-8"><a href="#cb22-8" tabindex="-1"></a>                    Polity_score <span class="sc">+</span> </span>
<span id="cb22-9"><a href="#cb22-9" tabindex="-1"></a>                    GDP_capita_logged <span class="sc">+</span> </span>
<span id="cb22-10"><a href="#cb22-10" tabindex="-1"></a>                    women_rep_lag <span class="sc">+</span> </span>
<span id="cb22-11"><a href="#cb22-11" tabindex="-1"></a>                    Corruption <span class="sc">+</span></span>
<span id="cb22-12"><a href="#cb22-12" tabindex="-1"></a>                    Presidentialism <span class="sc">+</span></span>
<span id="cb22-13"><a href="#cb22-13" tabindex="-1"></a>                    (<span class="dv">1</span> <span class="sc">|</span> Country_year), <span class="at">data =</span> Americas_analysis)</span></code></pre></div>
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" tabindex="-1"></a><span class="fu">summary</span>(Americas_hyp1_generation)</span></code></pre></div>
<pre><code>## Linear mixed model fit by REML [&#39;lmerMod&#39;]
## Formula: Satisfaction ~ quota_effect + Female + Ideology + Education +  
##     Income + Age + Generation + Polity_score + GDP_capita_logged +  
##     women_rep_lag + Corruption + Presidentialism + (1 | Country_year)
##    Data: Americas_analysis
## 
## REML criterion at convergence: -12185.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2357 -0.7416  0.2636  0.6848  3.0980 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Country_year (Intercept) 0.002809 0.0530  
##  Residual                 0.052024 0.2281  
## Number of obs: 107527, groups:  Country_year, 101
## 
## Fixed effects:
##                            Estimate Std. Error t value
## (Intercept)                0.576450   0.061859   9.319
## quota_effectNon-Effective  0.029363   0.015243   1.926
## quota_effectEffective     -0.015676   0.016364  -0.958
## FemaleWoman               -0.006799   0.001404  -4.843
## Ideology                   0.018504   0.002527   7.322
## Education                 -0.057061   0.003395 -16.808
## Income                     0.003646   0.004510   0.808
## Age                       -0.008654   0.012976  -0.667
## GenerationGen X           -0.003112   0.003056  -1.018
## GenerationGen Z           -0.004012   0.009256  -0.433
## GenerationMillennial      -0.003557   0.004832  -0.736
## GenerationSilent/Greatest  0.012638   0.003983   3.173
## Polity_score              -0.019056   0.049196  -0.387
## GDP_capita_logged          0.055877   0.042151   1.326
## women_rep_lag              0.037281   0.039570   0.942
## Corruption                -0.147260   0.034018  -4.329
## Presidentialism            0.068947   0.035364   1.950</code></pre>
<pre><code>## 
## Correlation matrix not shown by default, as p = 17 &gt; 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it</code></pre>
<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" tabindex="-1"></a><span class="fu">saveRDS</span>(Americas_hyp1_generation, <span class="st">&quot;Americas_hyp1_generation.rds&quot;</span>)</span></code></pre></div>
</div>
<div id="hyothesis-2-gender" class="section level4">
<h4>Hyothesis 2-Gender</h4>
<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" tabindex="-1"></a>Americas_hyp2 <span class="ot">&lt;-</span> <span class="fu">lmer</span>(Satisfaction <span class="sc">~</span> quota_effect<span class="sc">*</span>Female <span class="sc">+</span> </span>
<span id="cb27-2"><a href="#cb27-2" tabindex="-1"></a>                     Ideology <span class="sc">+</span> </span>
<span id="cb27-3"><a href="#cb27-3" tabindex="-1"></a>                    Education <span class="sc">+</span> </span>
<span id="cb27-4"><a href="#cb27-4" tabindex="-1"></a>                    Income <span class="sc">+</span></span>
<span id="cb27-5"><a href="#cb27-5" tabindex="-1"></a>                    Age <span class="sc">+</span> </span>
<span id="cb27-6"><a href="#cb27-6" tabindex="-1"></a>                    Polity_score <span class="sc">+</span> </span>
<span id="cb27-7"><a href="#cb27-7" tabindex="-1"></a>                    GDP_capita_logged <span class="sc">+</span> </span>
<span id="cb27-8"><a href="#cb27-8" tabindex="-1"></a>                    women_rep_lag <span class="sc">+</span> </span>
<span id="cb27-9"><a href="#cb27-9" tabindex="-1"></a>                    Corruption <span class="sc">+</span></span>
<span id="cb27-10"><a href="#cb27-10" tabindex="-1"></a>                    Presidentialism <span class="sc">+</span></span>
<span id="cb27-11"><a href="#cb27-11" tabindex="-1"></a>                    (Female <span class="sc">|</span> Country_year), <span class="at">data =</span> Americas_analysis)</span></code></pre></div>
<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" tabindex="-1"></a><span class="fu">summary</span>(Americas_hyp2)</span></code></pre></div>
<pre><code>## Linear mixed model fit by REML [&#39;lmerMod&#39;]
## Formula: Satisfaction ~ quota_effect * Female + Ideology + Education +  
##     Income + Age + Polity_score + GDP_capita_logged + women_rep_lag +  
##     Corruption + Presidentialism + (Female | Country_year)
##    Data: Americas_analysis
## 
## REML criterion at convergence: -12213.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2136 -0.7414  0.2642  0.6850  3.0950 
## 
## Random effects:
##  Groups       Name        Variance  Std.Dev. Corr 
##  Country_year (Intercept) 0.0029427 0.05425       
##               FemaleWoman 0.0001343 0.01159  -0.27
##  Residual                 0.0519952 0.22802       
## Number of obs: 107527, groups:  Country_year, 101
## 
## Fixed effects:
##                                        Estimate Std. Error t value
## (Intercept)                            0.557322   0.061385   9.079
## quota_effectNon-Effective              0.030785   0.015610   1.972
## quota_effectEffective                 -0.012918   0.016621  -0.777
## FemaleWoman                           -0.002709   0.002931  -0.924
## Ideology                               0.018576   0.002527   7.350
## Education                             -0.057297   0.003396 -16.871
## Income                                 0.003130   0.004512   0.694
## Age                                    0.010446   0.004120   2.535
## Polity_score                          -0.005933   0.048930  -0.121
## GDP_capita_logged                      0.052853   0.041915   1.261
## women_rep_lag                          0.034975   0.039339   0.889
## Corruption                            -0.142362   0.033824  -4.209
## Presidentialism                        0.068288   0.035152   1.943
## quota_effectNon-Effective:FemaleWoman -0.005680   0.004675  -1.215
## quota_effectEffective:FemaleWoman     -0.005851   0.004279  -1.367</code></pre>
<pre><code>## 
## Correlation matrix not shown by default, as p = 15 &gt; 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it</code></pre>
<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" tabindex="-1"></a><span class="fu">saveRDS</span>(Americas_hyp2, <span class="st">&quot;Americas_hyp2.rds&quot;</span>)</span></code></pre></div>
</div>
<div id="hypothesis-3a-ideology-hyothesis" class="section level4">
<h4>Hypothesis 3a-Ideology Hyothesis</h4>
<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" tabindex="-1"></a>Americas_hyp3a <span class="ot">&lt;-</span> <span class="fu">lmer</span>(Satisfaction <span class="sc">~</span> quota_effect<span class="sc">*</span>Ideology <span class="sc">+</span></span>
<span id="cb32-2"><a href="#cb32-2" tabindex="-1"></a>                    Female <span class="sc">+</span> </span>
<span id="cb32-3"><a href="#cb32-3" tabindex="-1"></a>                    Education <span class="sc">+</span> </span>
<span id="cb32-4"><a href="#cb32-4" tabindex="-1"></a>                    Income <span class="sc">+</span></span>
<span id="cb32-5"><a href="#cb32-5" tabindex="-1"></a>                    Age <span class="sc">+</span> </span>
<span id="cb32-6"><a href="#cb32-6" tabindex="-1"></a>                    Polity_score <span class="sc">+</span> </span>
<span id="cb32-7"><a href="#cb32-7" tabindex="-1"></a>                    GDP_capita_logged <span class="sc">+</span> </span>
<span id="cb32-8"><a href="#cb32-8" tabindex="-1"></a>                    women_rep_lag <span class="sc">+</span> </span>
<span id="cb32-9"><a href="#cb32-9" tabindex="-1"></a>                    Corruption <span class="sc">+</span></span>
<span id="cb32-10"><a href="#cb32-10" tabindex="-1"></a>                    Presidentialism <span class="sc">+</span></span>
<span id="cb32-11"><a href="#cb32-11" tabindex="-1"></a>                    (Ideology <span class="sc">|</span> Country_year), </span>
<span id="cb32-12"><a href="#cb32-12" tabindex="-1"></a>                    <span class="at">data =</span> Americas_analysis)</span></code></pre></div>
<div class="sourceCode" id="cb33"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1" tabindex="-1"></a><span class="fu">summary</span>(Americas_hyp3a)</span></code></pre></div>
<pre><code>## Linear mixed model fit by REML [&#39;lmerMod&#39;]
## Formula: Satisfaction ~ quota_effect * Ideology + Female + Education +  
##     Income + Age + Polity_score + GDP_capita_logged + women_rep_lag +  
##     Corruption + Presidentialism + (Ideology | Country_year)
##    Data: Americas_analysis
## 
## REML criterion at convergence: -14489.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2729 -0.7424  0.2180  0.6869  3.5806 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev. Corr 
##  Country_year (Intercept) 0.007279 0.08532       
##               Ideology    0.014316 0.11965  -0.81
##  Residual                 0.050791 0.22537       
## Number of obs: 107527, groups:  Country_year, 101
## 
## Fixed effects:
##                                     Estimate Std. Error t value
## (Intercept)                         0.572703   0.059399   9.642
## quota_effectNon-Effective          -0.020896   0.023080  -0.905
## quota_effectEffective              -0.044004   0.022390  -1.965
## Ideology                           -0.011299   0.018946  -0.596
## FemaleWoman                        -0.006767   0.001387  -4.877
## Education                          -0.051715   0.003361 -15.387
## Income                              0.001781   0.004459   0.399
## Age                                 0.006573   0.004077   1.612
## Polity_score                       -0.006791   0.046588  -0.146
## GDP_capita_logged                   0.050462   0.039901   1.265
## women_rep_lag                       0.036094   0.037462   0.964
## Corruption                         -0.128178   0.032214  -3.979
## Presidentialism                     0.046678   0.033477   1.394
## quota_effectNon-Effective:Ideology  0.092597   0.031320   2.957
## quota_effectEffective:Ideology      0.051887   0.028150   1.843</code></pre>
<pre><code>## 
## Correlation matrix not shown by default, as p = 15 &gt; 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it</code></pre>
<div class="sourceCode" id="cb36"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1" tabindex="-1"></a><span class="fu">saveRDS</span>(Americas_hyp3a, <span class="st">&quot;Americas_hyp3a.rds&quot;</span>)</span></code></pre></div>
</div>
<div id="hypothesis-3b-quota-support" class="section level4">
<h4>Hypothesis 3b-Quota support</h4>
<div class="sourceCode" id="cb37"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb37-1"><a href="#cb37-1" tabindex="-1"></a>Americas_hyp3b <span class="ot">&lt;-</span> <span class="fu">lmer</span>(Satisfaction <span class="sc">~</span> quota_effect<span class="sc">*</span>Quota_support <span class="sc">+</span></span>
<span id="cb37-2"><a href="#cb37-2" tabindex="-1"></a>                      Female <span class="sc">+</span> </span>
<span id="cb37-3"><a href="#cb37-3" tabindex="-1"></a>                      Ideology <span class="sc">+</span></span>
<span id="cb37-4"><a href="#cb37-4" tabindex="-1"></a>                    Education <span class="sc">+</span> </span>
<span id="cb37-5"><a href="#cb37-5" tabindex="-1"></a>                    Income <span class="sc">+</span></span>
<span id="cb37-6"><a href="#cb37-6" tabindex="-1"></a>                    Age <span class="sc">+</span> </span>
<span id="cb37-7"><a href="#cb37-7" tabindex="-1"></a>                    Polity_score <span class="sc">+</span> </span>
<span id="cb37-8"><a href="#cb37-8" tabindex="-1"></a>                    GDP_capita_logged <span class="sc">+</span> </span>
<span id="cb37-9"><a href="#cb37-9" tabindex="-1"></a>                    women_rep_lag <span class="sc">+</span> </span>
<span id="cb37-10"><a href="#cb37-10" tabindex="-1"></a>                    Corruption <span class="sc">+</span></span>
<span id="cb37-11"><a href="#cb37-11" tabindex="-1"></a>                    Presidentialism <span class="sc">+</span></span>
<span id="cb37-12"><a href="#cb37-12" tabindex="-1"></a>                    (<span class="dv">1</span> <span class="sc">|</span> Country_year), <span class="at">data =</span> Americas_analysis)</span></code></pre></div>
<div class="sourceCode" id="cb38"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb38-1"><a href="#cb38-1" tabindex="-1"></a><span class="fu">summary</span>(Americas_hyp3b)</span></code></pre></div>
<pre><code>## Linear mixed model fit by REML [&#39;lmerMod&#39;]
## Formula: Satisfaction ~ quota_effect * Quota_support + Female + Ideology +  
##     Education + Income + Age + Polity_score + GDP_capita_logged +  
##     women_rep_lag + Corruption + Presidentialism + (1 | Country_year)
##    Data: Americas_analysis
## 
## REML criterion at convergence: -2360.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0788 -0.7335  0.2949  0.7379  2.6965 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Country_year (Intercept) 0.002414 0.04913 
##  Residual                 0.048678 0.22063 
## Number of obs: 13648, groups:  Country_year, 23
## 
## Fixed effects:
##                                          Estimate Std. Error t value
## (Intercept)                              0.599241   0.131991   4.540
## quota_effectNon-Effective                0.032100   0.036989   0.868
## quota_effectEffective                   -0.011503   0.030928  -0.372
## Quota_support                           -0.007081   0.011127  -0.636
## FemaleWoman                             -0.006707   0.003902  -1.719
## Ideology                                 0.035976   0.006565   5.480
## Education                               -0.058352   0.009631  -6.059
## Income                                  -0.009739   0.009210  -1.057
## Age                                      0.009574   0.011046   0.867
## Polity_score                            -0.025201   0.086924  -0.290
## GDP_capita_logged                        0.008976   0.103906   0.086
## women_rep_lag                           -0.013037   0.084353  -0.155
## Corruption                              -0.155476   0.070772  -2.197
## Presidentialism                          0.138343   0.073667   1.878
## quota_effectNon-Effective:Quota_support -0.022848   0.016246  -1.406
## quota_effectEffective:Quota_support      0.026709   0.014825   1.802</code></pre>
<pre><code>## 
## Correlation matrix not shown by default, as p = 16 &gt; 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it</code></pre>
<div class="sourceCode" id="cb41"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb41-1"><a href="#cb41-1" tabindex="-1"></a><span class="fu">saveRDS</span>(Americas_hyp3b, <span class="st">&quot;Americas_hyp3b.rds&quot;</span>)</span></code></pre></div>
</div>
<div id="hypothesis-4-corruption" class="section level4">
<h4>Hypothesis 4-Corruption</h4>
<div class="sourceCode" id="cb42"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb42-1"><a href="#cb42-1" tabindex="-1"></a>Americas_hyp4 <span class="ot">&lt;-</span> <span class="fu">lmer</span>(Satisfaction <span class="sc">~</span> quota_effect<span class="sc">*</span>Corruption <span class="sc">+</span></span>
<span id="cb42-2"><a href="#cb42-2" tabindex="-1"></a>                     Female <span class="sc">+</span> </span>
<span id="cb42-3"><a href="#cb42-3" tabindex="-1"></a>                     Ideology <span class="sc">+</span> </span>
<span id="cb42-4"><a href="#cb42-4" tabindex="-1"></a>                    Education <span class="sc">+</span> </span>
<span id="cb42-5"><a href="#cb42-5" tabindex="-1"></a>                    Income <span class="sc">+</span></span>
<span id="cb42-6"><a href="#cb42-6" tabindex="-1"></a>                    Age <span class="sc">+</span> </span>
<span id="cb42-7"><a href="#cb42-7" tabindex="-1"></a>                    Polity_score <span class="sc">+</span> </span>
<span id="cb42-8"><a href="#cb42-8" tabindex="-1"></a>                    GDP_capita_logged <span class="sc">+</span> </span>
<span id="cb42-9"><a href="#cb42-9" tabindex="-1"></a>                    women_rep_lag <span class="sc">+</span> </span>
<span id="cb42-10"><a href="#cb42-10" tabindex="-1"></a>                    Presidentialism <span class="sc">+</span></span>
<span id="cb42-11"><a href="#cb42-11" tabindex="-1"></a>                    (<span class="dv">1</span> <span class="sc">|</span> Country_year), <span class="at">data =</span> Americas_analysis)</span></code></pre></div>
<div class="sourceCode" id="cb43"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb43-1"><a href="#cb43-1" tabindex="-1"></a><span class="fu">summary</span>(Americas_hyp4)</span></code></pre></div>
<pre><code>## Linear mixed model fit by REML [&#39;lmerMod&#39;]
## Formula: Satisfaction ~ quota_effect * Corruption + Female + Ideology +  
##     Education + Income + Age + Polity_score + GDP_capita_logged +  
##     women_rep_lag + Presidentialism + (1 | Country_year)
##    Data: Americas_analysis
## 
## REML criterion at convergence: -12208.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2046 -0.7416  0.2625  0.6849  3.0987 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Country_year (Intercept) 0.002678 0.05175 
##  Residual                 0.052029 0.22810 
## Number of obs: 107527, groups:  Country_year, 101
## 
## Fixed effects:
##                                       Estimate Std. Error t value
## (Intercept)                           0.539055   0.061639   8.745
## quota_effectNon-Effective             0.068217   0.063509   1.074
## quota_effectEffective                 0.060270   0.034377   1.753
## Corruption                           -0.118160   0.035309  -3.346
## FemaleWoman                          -0.006973   0.001403  -4.970
## Ideology                              0.018570   0.002527   7.348
## Education                            -0.057342   0.003393 -16.898
## Income                                0.003249   0.004508   0.721
## Age                                   0.010437   0.004119   2.534
## Polity_score                         -0.002504   0.048470  -0.052
## GDP_capita_logged                     0.063453   0.042713   1.486
## women_rep_lag                         0.012838   0.040630   0.316
## Presidentialism                       0.080133   0.034951   2.293
## quota_effectNon-Effective:Corruption -0.060415   0.082119  -0.736
## quota_effectEffective:Corruption     -0.121444   0.048455  -2.506</code></pre>
<pre><code>## 
## Correlation matrix not shown by default, as p = 15 &gt; 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it</code></pre>
<div class="sourceCode" id="cb46"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb46-1"><a href="#cb46-1" tabindex="-1"></a><span class="fu">saveRDS</span>(Americas_hyp4, <span class="st">&quot;Americas_hyp4.rds&quot;</span>)</span></code></pre></div>
</div>
</div>
<div id="cses" class="section level3">
<h3>CSES</h3>
<p>Estimate CSES models for <strong><em>Study 1</em></strong></p>
<div class="sourceCode" id="cb47"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb47-1"><a href="#cb47-1" tabindex="-1"></a>CSES_analysis <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Analysis Data/CSES_analysis.rds&quot;</span>)</span></code></pre></div>
<div id="hyothesis-1a-and-1b-1" class="section level4">
<h4>Hyothesis 1a and 1b</h4>
<div class="sourceCode" id="cb48"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb48-1"><a href="#cb48-1" tabindex="-1"></a>CSES_hyp1 <span class="ot">&lt;-</span> <span class="fu">lmer</span>(Satisfaction <span class="sc">~</span> quota_effect <span class="sc">+</span></span>
<span id="cb48-2"><a href="#cb48-2" tabindex="-1"></a>                     Female <span class="sc">+</span> </span>
<span id="cb48-3"><a href="#cb48-3" tabindex="-1"></a>                     Ideology <span class="sc">+</span> </span>
<span id="cb48-4"><a href="#cb48-4" tabindex="-1"></a>                    Education <span class="sc">+</span> </span>
<span id="cb48-5"><a href="#cb48-5" tabindex="-1"></a>                    Income <span class="sc">+</span></span>
<span id="cb48-6"><a href="#cb48-6" tabindex="-1"></a>                    Age <span class="sc">+</span> </span>
<span id="cb48-7"><a href="#cb48-7" tabindex="-1"></a>                    Polity_score <span class="sc">+</span> </span>
<span id="cb48-8"><a href="#cb48-8" tabindex="-1"></a>                    GDP_capita_logged <span class="sc">+</span> </span>
<span id="cb48-9"><a href="#cb48-9" tabindex="-1"></a>                    women_rep_lag <span class="sc">+</span> </span>
<span id="cb48-10"><a href="#cb48-10" tabindex="-1"></a>                    Corruption <span class="sc">+</span></span>
<span id="cb48-11"><a href="#cb48-11" tabindex="-1"></a>                    Presidentialism <span class="sc">+</span></span>
<span id="cb48-12"><a href="#cb48-12" tabindex="-1"></a>                    (<span class="dv">1</span> <span class="sc">|</span> Country_year), <span class="at">data =</span> CSES_analysis)</span></code></pre></div>
<div class="sourceCode" id="cb49"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb49-1"><a href="#cb49-1" tabindex="-1"></a><span class="fu">summary</span>(CSES_hyp1)</span></code></pre></div>
<pre><code>## Linear mixed model fit by REML [&#39;lmerMod&#39;]
## Formula: Satisfaction ~ quota_effect + Female + Ideology + Education +  
##     Income + Age + Polity_score + GDP_capita_logged + women_rep_lag +  
##     Corruption + Presidentialism + (1 | Country_year)
##    Data: CSES_analysis
## 
## REML criterion at convergence: -626.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2969 -0.5951  0.0896  0.6081  3.4652 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Country_year (Intercept) 0.007328 0.08561 
##  Residual                 0.058028 0.24089 
## Number of obs: 156300, groups:  Country_year, 144
## 
## Fixed effects:
##                            Estimate Std. Error t value
## (Intercept)                0.439630   0.093473   4.703
## quota_effectNon-Effective  0.035672   0.031459   1.134
## quota_effectEffective     -0.070964   0.021506  -3.300
## FemaleWoman               -0.005324   0.001227  -4.337
## Ideology                   0.089288   0.002506  35.625
## Education                  0.024348   0.002441   9.974
## Income                     0.049354   0.001920  25.707
## Age                        0.007716   0.003371   2.289
## Polity_score              -0.032813   0.073857  -0.444
## GDP_capita_logged          0.057649   0.068351   0.843
## women_rep_lag              0.097325   0.034612   2.812
## Corruption                -0.192551   0.063490  -3.033
## Presidentialism            0.012733   0.095840   0.133</code></pre>
<pre><code>## 
## Correlation matrix not shown by default, as p = 13 &gt; 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it</code></pre>
<div class="sourceCode" id="cb52"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb52-1"><a href="#cb52-1" tabindex="-1"></a><span class="fu">saveRDS</span>(CSES_hyp1, <span class="st">&quot;CSES_hyp1.rds&quot;</span>)</span></code></pre></div>
</div>
<div id="generation-robustness-1" class="section level4">
<h4>Generation Robustness</h4>
<div class="sourceCode" id="cb53"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb53-1"><a href="#cb53-1" tabindex="-1"></a>CSES_hyp1_generation <span class="ot">&lt;-</span> <span class="fu">lmer</span>(Satisfaction <span class="sc">~</span> quota_effect <span class="sc">+</span></span>
<span id="cb53-2"><a href="#cb53-2" tabindex="-1"></a>                     Female <span class="sc">+</span> </span>
<span id="cb53-3"><a href="#cb53-3" tabindex="-1"></a>                     Ideology <span class="sc">+</span> </span>
<span id="cb53-4"><a href="#cb53-4" tabindex="-1"></a>                    Education <span class="sc">+</span> </span>
<span id="cb53-5"><a href="#cb53-5" tabindex="-1"></a>                    Income <span class="sc">+</span></span>
<span id="cb53-6"><a href="#cb53-6" tabindex="-1"></a>                    Age <span class="sc">+</span> </span>
<span id="cb53-7"><a href="#cb53-7" tabindex="-1"></a>                    Generation <span class="sc">+</span> </span>
<span id="cb53-8"><a href="#cb53-8" tabindex="-1"></a>                    Polity_score <span class="sc">+</span> </span>
<span id="cb53-9"><a href="#cb53-9" tabindex="-1"></a>                    GDP_capita_logged <span class="sc">+</span> </span>
<span id="cb53-10"><a href="#cb53-10" tabindex="-1"></a>                    women_rep_lag <span class="sc">+</span> </span>
<span id="cb53-11"><a href="#cb53-11" tabindex="-1"></a>                    Corruption <span class="sc">+</span></span>
<span id="cb53-12"><a href="#cb53-12" tabindex="-1"></a>                    Presidentialism <span class="sc">+</span></span>
<span id="cb53-13"><a href="#cb53-13" tabindex="-1"></a>                    (<span class="dv">1</span> <span class="sc">|</span> Country_year), <span class="at">data =</span> CSES_analysis)</span></code></pre></div>
<div class="sourceCode" id="cb54"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb54-1"><a href="#cb54-1" tabindex="-1"></a><span class="fu">summary</span>(CSES_hyp1_generation)</span></code></pre></div>
<pre><code>## Linear mixed model fit by REML [&#39;lmerMod&#39;]
## Formula: Satisfaction ~ quota_effect + Female + Ideology + Education +  
##     Income + Age + Generation + Polity_score + GDP_capita_logged +  
##     women_rep_lag + Corruption + Presidentialism + (1 | Country_year)
##    Data: CSES_analysis
## 
## REML criterion at convergence: -649.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2767 -0.5952  0.0898  0.6066  3.4516 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Country_year (Intercept) 0.007388 0.08595 
##  Residual                 0.058007 0.24085 
## Number of obs: 156300, groups:  Country_year, 144
## 
## Fixed effects:
##                            Estimate Std. Error t value
## (Intercept)                0.427844   0.093887   4.557
## quota_effectNon-Effective  0.034916   0.031586   1.105
## quota_effectEffective     -0.071546   0.021594  -3.313
## FemaleWoman               -0.005140   0.001228  -4.187
## Ideology                   0.088700   0.002507  35.375
## Education                  0.024594   0.002445  10.058
## Income                     0.051708   0.001948  26.539
## Age                        0.026869   0.009625   2.792
## GenerationGen X            0.009494   0.002381   3.988
## GenerationGen Z            0.060351   0.013659   4.418
## GenerationMillennial       0.016848   0.003866   4.358
## GenerationSilent/Greatest  0.004847   0.002707   1.791
## Polity_score              -0.031027   0.074154  -0.418
## GDP_capita_logged          0.054392   0.068642   0.792
## women_rep_lag              0.094587   0.034758   2.721
## Corruption                -0.197599   0.063766  -3.099
## Presidentialism            0.016456   0.096231   0.171</code></pre>
<pre><code>## 
## Correlation matrix not shown by default, as p = 17 &gt; 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it</code></pre>
<div class="sourceCode" id="cb57"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb57-1"><a href="#cb57-1" tabindex="-1"></a><span class="fu">saveRDS</span>(CSES_hyp1_generation, <span class="st">&quot;CSES_hyp1_generation.rds&quot;</span>)</span></code></pre></div>
</div>
<div id="hypothesis-2-gender" class="section level4">
<h4>Hypothesis 2-Gender</h4>
<div class="sourceCode" id="cb58"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb58-1"><a href="#cb58-1" tabindex="-1"></a>CSES_hyp2 <span class="ot">&lt;-</span> <span class="fu">lmer</span>(Satisfaction <span class="sc">~</span> quota_effect<span class="sc">*</span>Female <span class="sc">+</span> </span>
<span id="cb58-2"><a href="#cb58-2" tabindex="-1"></a>                     Ideology <span class="sc">+</span> </span>
<span id="cb58-3"><a href="#cb58-3" tabindex="-1"></a>                    Education <span class="sc">+</span> </span>
<span id="cb58-4"><a href="#cb58-4" tabindex="-1"></a>                    Income <span class="sc">+</span></span>
<span id="cb58-5"><a href="#cb58-5" tabindex="-1"></a>                    Age <span class="sc">+</span> </span>
<span id="cb58-6"><a href="#cb58-6" tabindex="-1"></a>                    Polity_score <span class="sc">+</span> </span>
<span id="cb58-7"><a href="#cb58-7" tabindex="-1"></a>                    GDP_capita_logged <span class="sc">+</span> </span>
<span id="cb58-8"><a href="#cb58-8" tabindex="-1"></a>                    women_rep_lag <span class="sc">+</span> </span>
<span id="cb58-9"><a href="#cb58-9" tabindex="-1"></a>                    Corruption <span class="sc">+</span></span>
<span id="cb58-10"><a href="#cb58-10" tabindex="-1"></a>                    Presidentialism <span class="sc">+</span></span>
<span id="cb58-11"><a href="#cb58-11" tabindex="-1"></a>                    (Female <span class="sc">|</span> Country_year), <span class="at">data =</span> CSES_analysis)</span></code></pre></div>
<div class="sourceCode" id="cb59"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb59-1"><a href="#cb59-1" tabindex="-1"></a><span class="fu">summary</span>(CSES_hyp2)</span></code></pre></div>
<pre><code>## Linear mixed model fit by REML [&#39;lmerMod&#39;]
## Formula: Satisfaction ~ quota_effect * Female + Ideology + Education +  
##     Income + Age + Polity_score + GDP_capita_logged + women_rep_lag +  
##     Corruption + Presidentialism + (Female | Country_year)
##    Data: CSES_analysis
## 
## REML criterion at convergence: -682.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3321 -0.5949  0.0903  0.6086  3.4569 
## 
## Random effects:
##  Groups       Name        Variance  Std.Dev. Corr 
##  Country_year (Intercept) 0.0076719 0.08759       
##               FemaleWoman 0.0003304 0.01818  -0.26
##  Residual                 0.0579555 0.24074       
## Number of obs: 156300, groups:  Country_year, 144
## 
## Fixed effects:
##                                        Estimate Std. Error t value
## (Intercept)                            0.420220   0.092861   4.525
## quota_effectNon-Effective              0.042448   0.032115   1.322
## quota_effectEffective                 -0.073413   0.022020  -3.334
## FemaleWoman                           -0.003560   0.002242  -1.588
## Ideology                               0.089303   0.002508  35.609
## Education                              0.024344   0.002444   9.963
## Income                                 0.049147   0.001921  25.590
## Age                                    0.007687   0.003371   2.280
## Polity_score                          -0.025153   0.073372  -0.343
## GDP_capita_logged                      0.065106   0.067893   0.959
## women_rep_lag                          0.104395   0.034364   3.038
## Corruption                            -0.185593   0.063068  -2.943
## Presidentialism                        0.025522   0.095161   0.268
## quota_effectNon-Effective:FemaleWoman -0.010989   0.008058  -1.364
## quota_effectEffective:FemaleWoman      0.003103   0.005823   0.533</code></pre>
<pre><code>## 
## Correlation matrix not shown by default, as p = 15 &gt; 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it</code></pre>
<div class="sourceCode" id="cb62"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb62-1"><a href="#cb62-1" tabindex="-1"></a><span class="fu">saveRDS</span>(CSES_hyp2, <span class="st">&quot;CSES_hyp2.rds&quot;</span>)</span></code></pre></div>
</div>
<div id="hyothesis-3a-ideology" class="section level4">
<h4>Hyothesis 3a-Ideology</h4>
<div class="sourceCode" id="cb63"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb63-1"><a href="#cb63-1" tabindex="-1"></a>CSES_hyp3a <span class="ot">&lt;-</span> <span class="fu">lmer</span>(Satisfaction <span class="sc">~</span> quota_effect<span class="sc">*</span>Ideology <span class="sc">+</span> </span>
<span id="cb63-2"><a href="#cb63-2" tabindex="-1"></a>                     Female <span class="sc">+</span> </span>
<span id="cb63-3"><a href="#cb63-3" tabindex="-1"></a>                    Education <span class="sc">+</span> </span>
<span id="cb63-4"><a href="#cb63-4" tabindex="-1"></a>                    Income <span class="sc">+</span></span>
<span id="cb63-5"><a href="#cb63-5" tabindex="-1"></a>                    Age <span class="sc">+</span> </span>
<span id="cb63-6"><a href="#cb63-6" tabindex="-1"></a>                    Polity_score <span class="sc">+</span> </span>
<span id="cb63-7"><a href="#cb63-7" tabindex="-1"></a>                    GDP_capita_logged <span class="sc">+</span> </span>
<span id="cb63-8"><a href="#cb63-8" tabindex="-1"></a>                    women_rep_lag <span class="sc">+</span> </span>
<span id="cb63-9"><a href="#cb63-9" tabindex="-1"></a>                    Corruption <span class="sc">+</span></span>
<span id="cb63-10"><a href="#cb63-10" tabindex="-1"></a>                    Presidentialism <span class="sc">+</span></span>
<span id="cb63-11"><a href="#cb63-11" tabindex="-1"></a>                    (Ideology <span class="sc">|</span> Country_year), <span class="at">data =</span> CSES_analysis)</span></code></pre></div>
<div class="sourceCode" id="cb64"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb64-1"><a href="#cb64-1" tabindex="-1"></a><span class="fu">summary</span>(CSES_hyp3a)</span></code></pre></div>
<pre><code>## Linear mixed model fit by REML [&#39;lmerMod&#39;]
## Formula: Satisfaction ~ quota_effect * Ideology + Female + Education +  
##     Income + Age + Polity_score + GDP_capita_logged + women_rep_lag +  
##     Corruption + Presidentialism + (Ideology | Country_year)
##    Data: CSES_analysis
## 
## REML criterion at convergence: -3150
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2776 -0.5875  0.0795  0.6097  3.7276 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev. Corr 
##  Country_year (Intercept) 0.01425  0.1194        
##               Ideology    0.02027  0.1424   -0.69
##  Residual                 0.05693  0.2386        
## Number of obs: 156300, groups:  Country_year, 144
## 
## Fixed effects:
##                                     Estimate Std. Error t value
## (Intercept)                         0.418766   0.094243   4.443
## quota_effectNon-Effective           0.087800   0.042175   2.082
## quota_effectEffective              -0.069366   0.029523  -2.350
## Ideology                            0.101176   0.013757   7.354
## FemaleWoman                        -0.005541   0.001218  -4.550
## Education                           0.024040   0.002423   9.921
## Income                              0.048798   0.001907  25.595
## Age                                 0.010619   0.003355   3.165
## Polity_score                       -0.027139   0.074197  -0.366
## GDP_capita_logged                   0.069535   0.068675   1.013
## women_rep_lag                       0.092013   0.034779   2.646
## Corruption                         -0.183686   0.063792  -2.879
## Presidentialism                     0.014370   0.096295   0.149
## quota_effectNon-Effective:Ideology -0.092321   0.048066  -1.921
## quota_effectEffective:Ideology     -0.013093   0.034776  -0.376</code></pre>
<pre><code>## 
## Correlation matrix not shown by default, as p = 15 &gt; 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it</code></pre>
<div class="sourceCode" id="cb67"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb67-1"><a href="#cb67-1" tabindex="-1"></a><span class="fu">saveRDS</span>(CSES_hyp3a, <span class="st">&quot;CSES_hyp3a.rds&quot;</span>)</span></code></pre></div>
</div>
<div id="hypothesis-4-corruption-1" class="section level4">
<h4>Hypothesis 4-Corruption</h4>
<div class="sourceCode" id="cb68"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb68-1"><a href="#cb68-1" tabindex="-1"></a>CSES_hyp4 <span class="ot">&lt;-</span> <span class="fu">lmer</span>(Satisfaction <span class="sc">~</span> quota_effect<span class="sc">*</span>Corruption <span class="sc">+</span></span>
<span id="cb68-2"><a href="#cb68-2" tabindex="-1"></a>                     Female <span class="sc">+</span> </span>
<span id="cb68-3"><a href="#cb68-3" tabindex="-1"></a>                     Ideology <span class="sc">+</span> </span>
<span id="cb68-4"><a href="#cb68-4" tabindex="-1"></a>                    Education <span class="sc">+</span> </span>
<span id="cb68-5"><a href="#cb68-5" tabindex="-1"></a>                    Income <span class="sc">+</span></span>
<span id="cb68-6"><a href="#cb68-6" tabindex="-1"></a>                    Age <span class="sc">+</span> </span>
<span id="cb68-7"><a href="#cb68-7" tabindex="-1"></a>                    Polity_score <span class="sc">+</span> </span>
<span id="cb68-8"><a href="#cb68-8" tabindex="-1"></a>                    GDP_capita_logged <span class="sc">+</span> </span>
<span id="cb68-9"><a href="#cb68-9" tabindex="-1"></a>                    women_rep_lag <span class="sc">+</span> </span>
<span id="cb68-10"><a href="#cb68-10" tabindex="-1"></a>                    Presidentialism <span class="sc">+</span></span>
<span id="cb68-11"><a href="#cb68-11" tabindex="-1"></a>                    (<span class="dv">1</span> <span class="sc">|</span> Country_year), <span class="at">data =</span> CSES_analysis)</span></code></pre></div>
<div class="sourceCode" id="cb69"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb69-1"><a href="#cb69-1" tabindex="-1"></a><span class="fu">summary</span>(CSES_hyp4)</span></code></pre></div>
<pre><code>## Linear mixed model fit by REML [&#39;lmerMod&#39;]
## Formula: Satisfaction ~ quota_effect * Corruption + Female + Ideology +  
##     Education + Income + Age + Polity_score + GDP_capita_logged +  
##     women_rep_lag + Presidentialism + (1 | Country_year)
##    Data: CSES_analysis
## 
## REML criterion at convergence: -624.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2968 -0.5953  0.0895  0.6082  3.4654 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Country_year (Intercept) 0.007195 0.08482 
##  Residual                 0.058028 0.24089 
## Number of obs: 156300, groups:  Country_year, 144
## 
## Fixed effects:
##                                       Estimate Std. Error t value
## (Intercept)                           0.456189   0.093400   4.884
## quota_effectNon-Effective            -0.040047   0.053949  -0.742
## quota_effectEffective                -0.108143   0.034968  -3.093
## Corruption                           -0.264847   0.072091  -3.674
## FemaleWoman                          -0.005322   0.001227  -4.336
## Ideology                              0.089273   0.002506  35.619
## Education                             0.024342   0.002441   9.972
## Income                                0.049352   0.001920  25.707
## Age                                   0.007726   0.003371   2.292
## Polity_score                         -0.040526   0.073289  -0.553
## GDP_capita_logged                     0.063847   0.068384   0.934
## women_rep_lag                         0.079814   0.035599   2.242
## Presidentialism                       0.072560   0.099079   0.732
## quota_effectNon-Effective:Corruption  0.187403   0.104912   1.786
## quota_effectEffective:Corruption      0.117682   0.078938   1.491</code></pre>
<pre><code>## 
## Correlation matrix not shown by default, as p = 15 &gt; 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it</code></pre>
<div class="sourceCode" id="cb72"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb72-1"><a href="#cb72-1" tabindex="-1"></a><span class="fu">saveRDS</span>(CSES_hyp4, <span class="st">&quot;CSES_hyp4.rds&quot;</span>)</span></code></pre></div>
</div>
</div>
<div id="eurobarometer" class="section level3">
<h3>Eurobarometer</h3>
<p>Estimate Eurobarometer models for <strong><em>Study
1</em></strong></p>
<div class="sourceCode" id="cb73"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb73-1"><a href="#cb73-1" tabindex="-1"></a>Euro_analysis <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Analysis Data/Euro_analysis.rds&quot;</span>)</span></code></pre></div>
<div id="hypohesis-1a-and-1b" class="section level4">
<h4>Hypohesis 1a and 1b</h4>
<div class="sourceCode" id="cb74"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb74-1"><a href="#cb74-1" tabindex="-1"></a>Euro_hyp1 <span class="ot">&lt;-</span> <span class="fu">lmer</span>(Satisfaction <span class="sc">~</span> quota_effect <span class="sc">+</span> </span>
<span id="cb74-2"><a href="#cb74-2" tabindex="-1"></a>                    Female <span class="sc">+</span></span>
<span id="cb74-3"><a href="#cb74-3" tabindex="-1"></a>                    Ideology <span class="sc">+</span> </span>
<span id="cb74-4"><a href="#cb74-4" tabindex="-1"></a>                    Age_education <span class="sc">+</span> </span>
<span id="cb74-5"><a href="#cb74-5" tabindex="-1"></a>                    Age <span class="sc">+</span> </span>
<span id="cb74-6"><a href="#cb74-6" tabindex="-1"></a>                    Polity_score <span class="sc">+</span></span>
<span id="cb74-7"><a href="#cb74-7" tabindex="-1"></a>                    GDP_capita_logged  <span class="sc">+</span></span>
<span id="cb74-8"><a href="#cb74-8" tabindex="-1"></a>                    women_rep_lag <span class="sc">+</span> </span>
<span id="cb74-9"><a href="#cb74-9" tabindex="-1"></a>                    Corruption <span class="sc">+</span></span>
<span id="cb74-10"><a href="#cb74-10" tabindex="-1"></a>                    Presidentialism <span class="sc">+</span> </span>
<span id="cb74-11"><a href="#cb74-11" tabindex="-1"></a>                    (<span class="dv">1</span> <span class="sc">|</span> Country_year), <span class="at">data =</span> Euro_analysis)</span></code></pre></div>
<div class="sourceCode" id="cb75"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb75-1"><a href="#cb75-1" tabindex="-1"></a><span class="fu">summary</span>(Euro_hyp1)</span></code></pre></div>
<pre><code>## Linear mixed model fit by REML [&#39;lmerMod&#39;]
## Formula: Satisfaction ~ quota_effect + Female + Ideology + Age_education +  
##     Age + Polity_score + GDP_capita_logged + women_rep_lag +  
##     Corruption + Presidentialism + (1 | Country_year)
##    Data: Euro_analysis
## 
## REML criterion at convergence: 75551.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2327 -0.7111  0.1778  0.6162  3.2325 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Country_year (Intercept) 0.005767 0.07594 
##  Residual                 0.066400 0.25768 
## Number of obs: 581321, groups:  Country_year, 521
## 
## Fixed effects:
##                             Estimate Std. Error t value
## (Intercept)                0.4241075  0.0237269  17.875
## quota_effectNon-Effective -0.0124941  0.0201724  -0.619
## quota_effectEffective     -0.0804360  0.0150675  -5.338
## FemaleWoman               -0.0047655  0.0006784  -7.024
## Ideology                   0.0890858  0.0014625  60.915
## Age_education              0.0469185  0.0012707  36.923
## Age                        0.0043956  0.0018077   2.432
## Polity_score              -0.0074252  0.0189856  -0.391
## GDP_capita_logged          0.1744276  0.0242619   7.189
## women_rep_lag              0.0118422  0.0195991   0.604
## Corruption                -0.2833904  0.0387782  -7.308
## Presidentialism           -0.1557594  0.1072762  -1.452
## 
## Correlation of Fixed Effects:
##             (Intr) qt_N-E qt_ffE FmlWmn Idelgy Ag_dct Age    Plty_s GDP_c_
## qt_ffctNn-E  0.006                                                        
## qt_ffctEffc -0.027  0.052                                                 
## FemaleWoman -0.017  0.000  0.000                                          
## Ideology    -0.031  0.002  0.003  0.001                                   
## Age_educatn -0.031 -0.003  0.002  0.054 -0.014                            
## Age         -0.029 -0.004  0.000  0.024 -0.074  0.440                     
## Polity_scor -0.798  0.099  0.076  0.001  0.001  0.005  0.001              
## GDP_cpt_lgg -0.064 -0.053 -0.010  0.000  0.004 -0.010 -0.007 -0.267       
## women_rp_lg -0.334 -0.041 -0.170 -0.002  0.001 -0.020 -0.015  0.187 -0.566
## Corruption   0.116  0.029 -0.006 -0.002  0.001 -0.007 -0.006 -0.126  0.459
## Presidntlsm -0.557 -0.142 -0.013  0.001  0.000  0.002  0.001  0.337 -0.362
##             wmn_r_ Crrptn
## qt_ffctNn-E              
## qt_ffctEffc              
## FemaleWoman              
## Ideology                 
## Age_educatn              
## Age                      
## Polity_scor              
## GDP_cpt_lgg              
## women_rp_lg              
## Corruption  -0.323       
## Presidntlsm  0.521 -0.755</code></pre>
<div class="sourceCode" id="cb77"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb77-1"><a href="#cb77-1" tabindex="-1"></a><span class="fu">saveRDS</span>(Euro_hyp1, <span class="st">&quot;Euro_hyp1.rds&quot;</span>)</span></code></pre></div>
</div>
<div id="generation-robustness-2" class="section level4">
<h4>Generation Robustness</h4>
<div class="sourceCode" id="cb78"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb78-1"><a href="#cb78-1" tabindex="-1"></a>Euro_generation_hyp1 <span class="ot">&lt;-</span> <span class="fu">lmer</span>(Satisfaction <span class="sc">~</span> quota_effect <span class="sc">+</span> </span>
<span id="cb78-2"><a href="#cb78-2" tabindex="-1"></a>                    Female <span class="sc">+</span></span>
<span id="cb78-3"><a href="#cb78-3" tabindex="-1"></a>                    Ideology <span class="sc">+</span> </span>
<span id="cb78-4"><a href="#cb78-4" tabindex="-1"></a>                    Age_education <span class="sc">+</span> </span>
<span id="cb78-5"><a href="#cb78-5" tabindex="-1"></a>                    Age <span class="sc">+</span> </span>
<span id="cb78-6"><a href="#cb78-6" tabindex="-1"></a>                    Generation <span class="sc">+</span> </span>
<span id="cb78-7"><a href="#cb78-7" tabindex="-1"></a>                    Polity_score <span class="sc">+</span></span>
<span id="cb78-8"><a href="#cb78-8" tabindex="-1"></a>                    GDP_capita_logged  <span class="sc">+</span></span>
<span id="cb78-9"><a href="#cb78-9" tabindex="-1"></a>                    women_rep_lag <span class="sc">+</span> </span>
<span id="cb78-10"><a href="#cb78-10" tabindex="-1"></a>                    Corruption <span class="sc">+</span></span>
<span id="cb78-11"><a href="#cb78-11" tabindex="-1"></a>                    Presidentialism <span class="sc">+</span> </span>
<span id="cb78-12"><a href="#cb78-12" tabindex="-1"></a>                    (<span class="dv">1</span> <span class="sc">|</span> Country_year), <span class="at">data =</span> Euro_analysis)</span></code></pre></div>
<div class="sourceCode" id="cb79"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb79-1"><a href="#cb79-1" tabindex="-1"></a><span class="fu">summary</span>(Euro_generation_hyp1)</span></code></pre></div>
<pre><code>## Linear mixed model fit by REML [&#39;lmerMod&#39;]
## Formula: Satisfaction ~ quota_effect + Female + Ideology + Age_education +  
##     Age + Generation + Polity_score + GDP_capita_logged + women_rep_lag +  
##     Corruption + Presidentialism + (1 | Country_year)
##    Data: Euro_analysis
## 
## REML criterion at convergence: 75263.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2757 -0.7110  0.1775  0.6170  3.2151 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Country_year (Intercept) 0.005908 0.07687 
##  Residual                 0.066361 0.25761 
## Number of obs: 581321, groups:  Country_year, 521
## 
## Fixed effects:
##                             Estimate Std. Error t value
## (Intercept)                0.4142777  0.0240185  17.248
## quota_effectNon-Effective -0.0148911  0.0204160  -0.729
## quota_effectEffective     -0.0838391  0.0152507  -5.497
## FemaleWoman               -0.0046599  0.0006783  -6.870
## Ideology                   0.0878357  0.0014646  59.973
## Age_education              0.0456795  0.0012723  35.904
## Age                        0.0436784  0.0038700  11.286
## GenerationGen X            0.0168571  0.0011792  14.295
## GenerationGen Z            0.0535424  0.0067330   7.952
## GenerationMillennial       0.0319162  0.0019970  15.982
## GenerationSilent/Greatest -0.0054846  0.0013459  -4.075
## Polity_score              -0.0026757  0.0192174  -0.139
## GDP_capita_logged          0.1618845  0.0245736   6.588
## women_rep_lag              0.0019937  0.0198502   0.100
## Corruption                -0.3046909  0.0392795  -7.757
## Presidentialism           -0.1474349  0.1085693  -1.358</code></pre>
<pre><code>## 
## Correlation matrix not shown by default, as p = 16 &gt; 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it</code></pre>
<div class="sourceCode" id="cb82"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb82-1"><a href="#cb82-1" tabindex="-1"></a><span class="fu">saveRDS</span>(Euro_generation_hyp1, <span class="st">&quot;Euro_hyp1_generation.rds&quot;</span>)</span></code></pre></div>
</div>
<div id="hyothesis-2-gender-1" class="section level4">
<h4>Hyothesis 2-Gender</h4>
<div class="sourceCode" id="cb83"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb83-1"><a href="#cb83-1" tabindex="-1"></a>Euro_hyp2 <span class="ot">&lt;-</span> <span class="fu">lmer</span>(Satisfaction <span class="sc">~</span> quota_effect<span class="sc">*</span>Female <span class="sc">+</span></span>
<span id="cb83-2"><a href="#cb83-2" tabindex="-1"></a>                    Ideology <span class="sc">+</span> </span>
<span id="cb83-3"><a href="#cb83-3" tabindex="-1"></a>                    Age_education <span class="sc">+</span> </span>
<span id="cb83-4"><a href="#cb83-4" tabindex="-1"></a>                    Age <span class="sc">+</span> </span>
<span id="cb83-5"><a href="#cb83-5" tabindex="-1"></a>                    Polity_score <span class="sc">+</span></span>
<span id="cb83-6"><a href="#cb83-6" tabindex="-1"></a>                    GDP_capita_logged  <span class="sc">+</span></span>
<span id="cb83-7"><a href="#cb83-7" tabindex="-1"></a>                    women_rep_lag <span class="sc">+</span> </span>
<span id="cb83-8"><a href="#cb83-8" tabindex="-1"></a>                    Corruption <span class="sc">+</span></span>
<span id="cb83-9"><a href="#cb83-9" tabindex="-1"></a>                    Presidentialism <span class="sc">+</span> </span>
<span id="cb83-10"><a href="#cb83-10" tabindex="-1"></a>                    (Female <span class="sc">|</span> Country_year), <span class="at">data =</span> Euro_analysis)</span></code></pre></div>
<div class="sourceCode" id="cb84"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb84-1"><a href="#cb84-1" tabindex="-1"></a><span class="fu">summary</span>(Euro_hyp2)</span></code></pre></div>
<pre><code>## Linear mixed model fit by REML [&#39;lmerMod&#39;]
## Formula: Satisfaction ~ quota_effect * Female + Ideology + Age_education +  
##     Age + Polity_score + GDP_capita_logged + women_rep_lag +  
##     Corruption + Presidentialism + (Female | Country_year)
##    Data: Euro_analysis
## 
## REML criterion at convergence: 75452
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2257 -0.7112  0.1783  0.6163  3.2414 
## 
## Random effects:
##  Groups       Name        Variance  Std.Dev. Corr 
##  Country_year (Intercept) 0.0059302 0.07701       
##               FemaleWoman 0.0001693 0.01301  -0.20
##  Residual                 0.0663563 0.25760       
## Number of obs: 581321, groups:  Country_year, 521
## 
## Fixed effects:
##                                         Estimate Std. Error t value
## (Intercept)                            0.4219334  0.0236773  17.820
## quota_effectNon-Effective             -0.0099845  0.0205636  -0.486
## quota_effectEffective                 -0.0825183  0.0153646  -5.371
## FemaleWoman                           -0.0049746  0.0009475  -5.250
## Ideology                               0.0888125  0.0014630  60.708
## Age_education                          0.0471996  0.0012719  37.108
## Age                                    0.0044801  0.0018085   2.477
## Polity_score                          -0.0048493  0.0189354  -0.256
## GDP_capita_logged                      0.1743349  0.0242006   7.204
## women_rep_lag                          0.0061540  0.0195481   0.315
## Corruption                            -0.2874958  0.0386733  -7.434
## Presidentialism                       -0.1334026  0.1070034  -1.247
## quota_effectNon-Effective:FemaleWoman -0.0070697  0.0056178  -1.258
## quota_effectEffective:FemaleWoman      0.0072803  0.0042464   1.714</code></pre>
<pre><code>## 
## Correlation matrix not shown by default, as p = 14 &gt; 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it</code></pre>
<div class="sourceCode" id="cb87"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb87-1"><a href="#cb87-1" tabindex="-1"></a><span class="fu">saveRDS</span>(Euro_hyp2, <span class="st">&quot;Euro_hyp2.rds&quot;</span>)</span></code></pre></div>
</div>
<div id="hyothesis-3a-ideology-1" class="section level4">
<h4>Hyothesis 3a-Ideology</h4>
<div class="sourceCode" id="cb88"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb88-1"><a href="#cb88-1" tabindex="-1"></a>Euro_hyp3a <span class="ot">&lt;-</span> <span class="fu">lmer</span>(Satisfaction <span class="sc">~</span> quota_effect<span class="sc">*</span>Ideology <span class="sc">+</span></span>
<span id="cb88-2"><a href="#cb88-2" tabindex="-1"></a>                    Female <span class="sc">+</span></span>
<span id="cb88-3"><a href="#cb88-3" tabindex="-1"></a>                    Age_education <span class="sc">+</span> </span>
<span id="cb88-4"><a href="#cb88-4" tabindex="-1"></a>                    Age <span class="sc">+</span> </span>
<span id="cb88-5"><a href="#cb88-5" tabindex="-1"></a>                    Polity_score <span class="sc">+</span></span>
<span id="cb88-6"><a href="#cb88-6" tabindex="-1"></a>                    GDP_capita_logged  <span class="sc">+</span></span>
<span id="cb88-7"><a href="#cb88-7" tabindex="-1"></a>                    women_rep_lag <span class="sc">+</span> </span>
<span id="cb88-8"><a href="#cb88-8" tabindex="-1"></a>                    Corruption <span class="sc">+</span></span>
<span id="cb88-9"><a href="#cb88-9" tabindex="-1"></a>                    Presidentialism <span class="sc">+</span> </span>
<span id="cb88-10"><a href="#cb88-10" tabindex="-1"></a>                    (Ideology <span class="sc">|</span> Country_year), <span class="at">data =</span> Euro_analysis)</span></code></pre></div>
<div class="sourceCode" id="cb89"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb89-1"><a href="#cb89-1" tabindex="-1"></a><span class="fu">summary</span>(Euro_hyp3a)</span></code></pre></div>
<pre><code>## Linear mixed model fit by REML [&#39;lmerMod&#39;]
## Formula: Satisfaction ~ quota_effect * Ideology + Female + Age_education +  
##     Age + Polity_score + GDP_capita_logged + women_rep_lag +  
##     Corruption + Presidentialism + (Ideology | Country_year)
##    Data: Euro_analysis
## 
## REML criterion at convergence: 65630.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2912 -0.7144  0.1709  0.6126  3.3042 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev. Corr 
##  Country_year (Intercept) 0.01070  0.1034        
##               Ideology    0.02093  0.1447   -0.69
##  Residual                 0.06511  0.2552        
## Number of obs: 581321, groups:  Country_year, 521
## 
## Fixed effects:
##                                      Estimate Std. Error t value
## (Intercept)                         0.4185017  0.0237352  17.632
## quota_effectNon-Effective          -0.0104793  0.0273155  -0.384
## quota_effectEffective              -0.0343825  0.0204652  -1.680
## Ideology                            0.0935101  0.0068427  13.666
## FemaleWoman                        -0.0054629  0.0006727  -8.120
## Age_education                       0.0458665  0.0012609  36.375
## Age                                 0.0017228  0.0017968   0.959
## Polity_score                       -0.0057031  0.0188090  -0.303
## GDP_capita_logged                   0.1771819  0.0240329   7.372
## women_rep_lag                       0.0122378  0.0194132   0.630
## Corruption                         -0.2789981  0.0384094  -7.264
## Presidentialism                    -0.1518036  0.1062578  -1.429
## quota_effectNon-Effective:Ideology  0.0006979  0.0380873   0.018
## quota_effectEffective:Ideology     -0.0974295  0.0286678  -3.399</code></pre>
<pre><code>## 
## Correlation matrix not shown by default, as p = 14 &gt; 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it</code></pre>
<div class="sourceCode" id="cb92"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb92-1"><a href="#cb92-1" tabindex="-1"></a><span class="fu">saveRDS</span>(Euro_hyp3a, <span class="st">&quot;Euro_hyp3a.rds&quot;</span>)</span></code></pre></div>
</div>
<div id="hypothesis-4-corruption-2" class="section level4">
<h4>Hypothesis 4-Corruption</h4>
<div class="sourceCode" id="cb93"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb93-1"><a href="#cb93-1" tabindex="-1"></a>Euro_hyp4 <span class="ot">&lt;-</span> <span class="fu">lmer</span>(Satisfaction <span class="sc">~</span> quota_effect<span class="sc">*</span>Corruption <span class="sc">+</span></span>
<span id="cb93-2"><a href="#cb93-2" tabindex="-1"></a>                    Female <span class="sc">+</span></span>
<span id="cb93-3"><a href="#cb93-3" tabindex="-1"></a>                    Ideology <span class="sc">+</span> </span>
<span id="cb93-4"><a href="#cb93-4" tabindex="-1"></a>                    Age_education <span class="sc">+</span> </span>
<span id="cb93-5"><a href="#cb93-5" tabindex="-1"></a>                    Age <span class="sc">+</span> </span>
<span id="cb93-6"><a href="#cb93-6" tabindex="-1"></a>                    Polity_score <span class="sc">+</span></span>
<span id="cb93-7"><a href="#cb93-7" tabindex="-1"></a>                    GDP_capita_logged  <span class="sc">+</span></span>
<span id="cb93-8"><a href="#cb93-8" tabindex="-1"></a>                    women_rep_lag <span class="sc">+</span> </span>
<span id="cb93-9"><a href="#cb93-9" tabindex="-1"></a>                    Presidentialism <span class="sc">+</span> </span>
<span id="cb93-10"><a href="#cb93-10" tabindex="-1"></a>                    (<span class="dv">1</span> <span class="sc">|</span> Country_year), <span class="at">data =</span> Euro_analysis)</span></code></pre></div>
<div class="sourceCode" id="cb94"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb94-1"><a href="#cb94-1" tabindex="-1"></a><span class="fu">summary</span>(Euro_hyp4)</span></code></pre></div>
<pre><code>## Linear mixed model fit by REML [&#39;lmerMod&#39;]
## Formula: Satisfaction ~ quota_effect * Corruption + Female + Ideology +  
##     Age_education + Age + Polity_score + GDP_capita_logged +  
##     women_rep_lag + Presidentialism + (1 | Country_year)
##    Data: Euro_analysis
## 
## REML criterion at convergence: 75526
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2326 -0.7111  0.1779  0.6163  3.2321 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Country_year (Intercept) 0.005444 0.07378 
##  Residual                 0.066400 0.25768 
## Number of obs: 581321, groups:  Country_year, 521
## 
## Fixed effects:
##                                        Estimate Std. Error t value
## (Intercept)                           0.4566181  0.0240998  18.947
## quota_effectNon-Effective            -0.1087840  0.0263529  -4.128
## quota_effectEffective                -0.0658952  0.0203656  -3.236
## Corruption                           -0.2782460  0.0379653  -7.329
## FemaleWoman                          -0.0047633  0.0006784  -7.021
## Ideology                              0.0890749  0.0014625  60.908
## Age_education                         0.0469108  0.0012707  36.917
## Age                                   0.0044064  0.0018076   2.438
## Polity_score                         -0.0285463  0.0191224  -1.493
## GDP_capita_logged                     0.2066403  0.0243303   8.493
## women_rep_lag                        -0.0179944  0.0197951  -0.909
## Presidentialism                      -0.3270907  0.1088211  -3.006
## quota_effectNon-Effective:Corruption  0.3891320  0.0712265   5.463
## quota_effectEffective:Corruption     -0.1298389  0.1254941  -1.035</code></pre>
<pre><code>## 
## Correlation matrix not shown by default, as p = 14 &gt; 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it</code></pre>
<div class="sourceCode" id="cb97"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb97-1"><a href="#cb97-1" tabindex="-1"></a><span class="fu">saveRDS</span>(Euro_hyp4, <span class="st">&quot;Euro_hyp4.rds&quot;</span>)</span></code></pre></div>
</div>
</div>
<div id="latinobarometer" class="section level3">
<h3>Latinobarometer</h3>
<p>Estimate Latinobarometer models for <strong><em>Study
1</em></strong></p>
<div class="sourceCode" id="cb98"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb98-1"><a href="#cb98-1" tabindex="-1"></a>Latino_analysis <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Analysis Data/Latino_analysis.rds&quot;</span>)</span></code></pre></div>
<div id="hyothesis-1a-and-1b-2" class="section level4">
<h4>Hyothesis 1a and 1b</h4>
<div class="sourceCode" id="cb99"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb99-1"><a href="#cb99-1" tabindex="-1"></a>Latino_hyp1 <span class="ot">&lt;-</span> <span class="fu">lmer</span>(Satisfaction <span class="sc">~</span> </span>
<span id="cb99-2"><a href="#cb99-2" tabindex="-1"></a>                      quota_effect <span class="sc">+</span> </span>
<span id="cb99-3"><a href="#cb99-3" tabindex="-1"></a>                      Female <span class="sc">+</span></span>
<span id="cb99-4"><a href="#cb99-4" tabindex="-1"></a>                      Ideology <span class="sc">+</span> </span>
<span id="cb99-5"><a href="#cb99-5" tabindex="-1"></a>                      Education <span class="sc">+</span> </span>
<span id="cb99-6"><a href="#cb99-6" tabindex="-1"></a>                      Age <span class="sc">+</span> </span>
<span id="cb99-7"><a href="#cb99-7" tabindex="-1"></a>                      Polity_score <span class="sc">+</span> </span>
<span id="cb99-8"><a href="#cb99-8" tabindex="-1"></a>                      GDP_capita_logged <span class="sc">+</span></span>
<span id="cb99-9"><a href="#cb99-9" tabindex="-1"></a>                      women_rep_lag <span class="sc">+</span></span>
<span id="cb99-10"><a href="#cb99-10" tabindex="-1"></a>                      Corruption <span class="sc">+</span> </span>
<span id="cb99-11"><a href="#cb99-11" tabindex="-1"></a>                      Presidentialism<span class="sc">+</span></span>
<span id="cb99-12"><a href="#cb99-12" tabindex="-1"></a>                      (<span class="dv">1</span> <span class="sc">|</span> Country_year),</span>
<span id="cb99-13"><a href="#cb99-13" tabindex="-1"></a>                      <span class="at">data =</span> Latino_analysis)</span></code></pre></div>
<div class="sourceCode" id="cb100"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb100-1"><a href="#cb100-1" tabindex="-1"></a><span class="fu">summary</span>(Latino_hyp1)</span></code></pre></div>
<pre><code>## Linear mixed model fit by REML [&#39;lmerMod&#39;]
## Formula: Satisfaction ~ quota_effect + Female + Ideology + Education +  
##     Age + Polity_score + GDP_capita_logged + women_rep_lag +  
##     Corruption + Presidentialism + (1 | Country_year)
##    Data: Latino_analysis
## 
## REML criterion at convergence: 56965.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9264 -0.6393 -0.0978  0.6935  3.2266 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Country_year (Intercept) 0.01220  0.1104  
##  Residual                 0.07163  0.2676  
## Number of obs: 274148, groups:  Country_year, 316
## 
## Fixed effects:
##                             Estimate Std. Error t value
## (Intercept)                2.459e-01  1.737e-01   1.416
## quota_effectNon-Effective -2.556e-02  1.796e-02  -1.424
## quota_effectEffective     -6.696e-02  1.788e-02  -3.745
## FemaleWoman               -7.175e-03  1.024e-03  -7.003
## Ideology                   3.295e-02  1.957e-03  16.840
## Education                 -2.074e-04  5.295e-05  -3.917
## Age                        1.885e-04  3.235e-05   5.828
## Polity_score               8.997e-03  4.830e-03   1.863
## GDP_capita_logged          2.252e-02  1.624e-02   1.387
## women_rep_lag              2.694e-04  9.282e-04   0.290
## Corruption                -2.576e-01  4.568e-02  -5.639
## Presidentialism            2.528e-01  4.797e-02   5.269
## 
## Correlation of Fixed Effects:
##             (Intr) qt_N-E qt_ffE FmlWmn Idelgy Eductn Age    Plty_s GDP_c_
## qt_ffctNn-E  0.251                                                        
## qt_ffctEffc  0.037  0.345                                                 
## FemaleWoman -0.002  0.000  0.000                                          
## Ideology    -0.008 -0.002 -0.001 -0.015                                   
## Education    0.000 -0.004 -0.008  0.009  0.006                            
## Age         -0.002  0.001 -0.001  0.021 -0.068 -0.006                     
## Polity_scor -0.549 -0.252  0.009  0.000  0.001  0.003 -0.001              
## GDP_cpt_lgg -0.959 -0.211 -0.016 -0.001  0.003 -0.006 -0.007  0.321       
## women_rp_lg  0.307 -0.012 -0.528  0.001  0.000  0.002  0.002 -0.221 -0.372
## Corruption  -0.457 -0.347 -0.338  0.001 -0.004  0.002  0.005  0.248  0.368
## Presidntlsm -0.321  0.027  0.310 -0.001  0.003  0.000 -0.003  0.422  0.270
##             wmn_r_ Crrptn
## qt_ffctNn-E              
## qt_ffctEffc              
## FemaleWoman              
## Ideology                 
## Education                
## Age                      
## Polity_scor              
## GDP_cpt_lgg              
## women_rp_lg              
## Corruption   0.199       
## Presidntlsm -0.440 -0.529</code></pre>
<div class="sourceCode" id="cb102"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb102-1"><a href="#cb102-1" tabindex="-1"></a><span class="fu">saveRDS</span>(Latino_hyp1, <span class="st">&quot;Latino_hyp1.rds&quot;</span>)</span></code></pre></div>
</div>
<div id="generation-robustness-3" class="section level4">
<h4>Generation Robustness</h4>
<div class="sourceCode" id="cb103"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb103-1"><a href="#cb103-1" tabindex="-1"></a>Latino_hyp1_generation <span class="ot">&lt;-</span> <span class="fu">lmer</span>(Satisfaction <span class="sc">~</span> </span>
<span id="cb103-2"><a href="#cb103-2" tabindex="-1"></a>                      quota_effect <span class="sc">+</span> </span>
<span id="cb103-3"><a href="#cb103-3" tabindex="-1"></a>                      Female <span class="sc">+</span></span>
<span id="cb103-4"><a href="#cb103-4" tabindex="-1"></a>                      Ideology <span class="sc">+</span> </span>
<span id="cb103-5"><a href="#cb103-5" tabindex="-1"></a>                      Education <span class="sc">+</span> </span>
<span id="cb103-6"><a href="#cb103-6" tabindex="-1"></a>                      Age <span class="sc">+</span> </span>
<span id="cb103-7"><a href="#cb103-7" tabindex="-1"></a>                      Generation <span class="sc">+</span> </span>
<span id="cb103-8"><a href="#cb103-8" tabindex="-1"></a>                      Polity_score <span class="sc">+</span> </span>
<span id="cb103-9"><a href="#cb103-9" tabindex="-1"></a>                      GDP_capita_logged <span class="sc">+</span></span>
<span id="cb103-10"><a href="#cb103-10" tabindex="-1"></a>                      women_rep_lag <span class="sc">+</span></span>
<span id="cb103-11"><a href="#cb103-11" tabindex="-1"></a>                      Corruption <span class="sc">+</span> </span>
<span id="cb103-12"><a href="#cb103-12" tabindex="-1"></a>                      Presidentialism<span class="sc">+</span></span>
<span id="cb103-13"><a href="#cb103-13" tabindex="-1"></a>                      (<span class="dv">1</span> <span class="sc">|</span> Country_year),</span>
<span id="cb103-14"><a href="#cb103-14" tabindex="-1"></a>                      <span class="at">data =</span> Latino_analysis)</span></code></pre></div>
<div class="sourceCode" id="cb104"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb104-1"><a href="#cb104-1" tabindex="-1"></a><span class="fu">summary</span>(Latino_hyp1_generation)</span></code></pre></div>
<pre><code>## Linear mixed model fit by REML [&#39;lmerMod&#39;]
## Formula: Satisfaction ~ quota_effect + Female + Ideology + Education +  
##     Age + Generation + Polity_score + GDP_capita_logged + women_rep_lag +  
##     Corruption + Presidentialism + (1 | Country_year)
##    Data: Latino_analysis
## 
## REML criterion at convergence: 56990.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9220 -0.6394 -0.0977  0.6941  3.2327 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Country_year (Intercept) 0.01225  0.1107  
##  Residual                 0.07163  0.2676  
## Number of obs: 274148, groups:  Country_year, 316
## 
## Fixed effects:
##                             Estimate Std. Error t value
## (Intercept)                2.456e-01  1.741e-01   1.411
## quota_effectNon-Effective -2.611e-02  1.800e-02  -1.451
## quota_effectEffective     -6.702e-02  1.792e-02  -3.740
## FemaleWoman               -7.165e-03  1.024e-03  -6.993
## Ideology                   3.291e-02  1.957e-03  16.816
## Education                 -1.893e-04  5.386e-05  -3.516
## Age                        4.579e-04  1.002e-04   4.570
## GenerationGen X            6.895e-03  2.088e-03   3.302
## GenerationGen Z            1.959e-02  7.246e-03   2.704
## GenerationMillennial       1.098e-02  3.257e-03   3.371
## GenerationSilent/Greatest -2.128e-03  2.610e-03  -0.815
## Polity_score               8.814e-03  4.841e-03   1.821
## GDP_capita_logged          2.130e-02  1.628e-02   1.308
## women_rep_lag              2.021e-04  9.306e-04   0.217
## Corruption                -2.598e-01  4.579e-02  -5.673
## Presidentialism            2.520e-01  4.808e-02   5.241</code></pre>
<pre><code>## 
## Correlation matrix not shown by default, as p = 16 &gt; 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it</code></pre>
<div class="sourceCode" id="cb107"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb107-1"><a href="#cb107-1" tabindex="-1"></a><span class="fu">saveRDS</span>(Latino_hyp1_generation, <span class="st">&quot;Latino_hyp1_generation.rds&quot;</span>)</span></code></pre></div>
</div>
<div id="hypothesis-2-gender-1" class="section level4">
<h4>Hypothesis 2-Gender</h4>
<div class="sourceCode" id="cb108"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb108-1"><a href="#cb108-1" tabindex="-1"></a>Latino_hyp2 <span class="ot">&lt;-</span> <span class="fu">lmer</span>(Satisfaction <span class="sc">~</span> quota_effect<span class="sc">*</span>Female <span class="sc">+</span></span>
<span id="cb108-2"><a href="#cb108-2" tabindex="-1"></a>                      Ideology <span class="sc">+</span> </span>
<span id="cb108-3"><a href="#cb108-3" tabindex="-1"></a>                      Education<span class="sc">+</span></span>
<span id="cb108-4"><a href="#cb108-4" tabindex="-1"></a>                      Age <span class="sc">+</span> </span>
<span id="cb108-5"><a href="#cb108-5" tabindex="-1"></a>                      Polity_score <span class="sc">+</span> </span>
<span id="cb108-6"><a href="#cb108-6" tabindex="-1"></a>                      GDP_capita_logged <span class="sc">+</span> </span>
<span id="cb108-7"><a href="#cb108-7" tabindex="-1"></a>                      women_rep_lag <span class="sc">+</span> </span>
<span id="cb108-8"><a href="#cb108-8" tabindex="-1"></a>                      Corruption <span class="sc">+</span> </span>
<span id="cb108-9"><a href="#cb108-9" tabindex="-1"></a>                      Presidentialism</span>
<span id="cb108-10"><a href="#cb108-10" tabindex="-1"></a>                      <span class="sc">+</span> (Female <span class="sc">|</span> Country_year),</span>
<span id="cb108-11"><a href="#cb108-11" tabindex="-1"></a>                      <span class="at">data =</span> Latino_analysis)</span></code></pre></div>
<div class="sourceCode" id="cb109"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb109-1"><a href="#cb109-1" tabindex="-1"></a><span class="fu">summary</span>(Latino_hyp2)</span></code></pre></div>
<pre><code>## Linear mixed model fit by REML [&#39;lmerMod&#39;]
## Formula: Satisfaction ~ quota_effect * Female + Ideology + Education +  
##     Age + Polity_score + GDP_capita_logged + women_rep_lag +  
##     Corruption + Presidentialism + (Female | Country_year)
##    Data: Latino_analysis
## 
## REML criterion at convergence: 56946.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9419 -0.6397 -0.0975  0.6918  3.2184 
## 
## Random effects:
##  Groups       Name        Variance  Std.Dev. Corr
##  Country_year (Intercept) 0.0119531 0.10933      
##               FemaleWoman 0.0001535 0.01239  0.16
##  Residual                 0.0715933 0.26757      
## Number of obs: 274148, groups:  Country_year, 316
## 
## Fixed effects:
##                                         Estimate Std. Error t value
## (Intercept)                            2.320e-01  1.725e-01   1.345
## quota_effectNon-Effective             -2.267e-02  1.784e-02  -1.271
## quota_effectEffective                 -6.837e-02  1.776e-02  -3.850
## FemaleWoman                           -5.089e-03  1.771e-03  -2.874
## Ideology                               3.294e-02  1.957e-03  16.834
## Education                             -2.108e-04  5.300e-05  -3.977
## Age                                    1.880e-04  3.236e-05   5.809
## Polity_score                           8.488e-03  4.798e-03   1.769
## GDP_capita_logged                      2.386e-02  1.612e-02   1.480
## women_rep_lag                          4.079e-04  9.219e-04   0.442
## Corruption                            -2.475e-01  4.537e-02  -5.456
## Presidentialism                        2.468e-01  4.764e-02   5.180
## quota_effectNon-Effective:FemaleWoman -8.324e-03  3.330e-03  -2.500
## quota_effectEffective:FemaleWoman     -1.535e-03  2.862e-03  -0.536</code></pre>
<pre><code>## 
## Correlation matrix not shown by default, as p = 14 &gt; 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it</code></pre>
<div class="sourceCode" id="cb112"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb112-1"><a href="#cb112-1" tabindex="-1"></a><span class="fu">saveRDS</span>(Latino_hyp2, <span class="st">&quot;Latino_hyp2.rds&quot;</span>)</span></code></pre></div>
</div>
<div id="hypothesis-3a-ideology" class="section level4">
<h4>Hypothesis 3a-Ideology</h4>
<div class="sourceCode" id="cb113"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb113-1"><a href="#cb113-1" tabindex="-1"></a>Latino_hyp3a <span class="ot">&lt;-</span> <span class="fu">lmer</span>(Satisfaction <span class="sc">~</span> quota_effect<span class="sc">*</span>Ideology <span class="sc">+</span> </span>
<span id="cb113-2"><a href="#cb113-2" tabindex="-1"></a>                      Female <span class="sc">+</span> </span>
<span id="cb113-3"><a href="#cb113-3" tabindex="-1"></a>                      Education <span class="sc">+</span> </span>
<span id="cb113-4"><a href="#cb113-4" tabindex="-1"></a>                      Age <span class="sc">+</span> </span>
<span id="cb113-5"><a href="#cb113-5" tabindex="-1"></a>                      Polity_score <span class="sc">+</span></span>
<span id="cb113-6"><a href="#cb113-6" tabindex="-1"></a>                      GDP_capita_logged <span class="sc">+</span> </span>
<span id="cb113-7"><a href="#cb113-7" tabindex="-1"></a>                      women_rep_lag <span class="sc">+</span> </span>
<span id="cb113-8"><a href="#cb113-8" tabindex="-1"></a>                      Corruption <span class="sc">+</span> </span>
<span id="cb113-9"><a href="#cb113-9" tabindex="-1"></a>                      Presidentialism <span class="sc">+</span></span>
<span id="cb113-10"><a href="#cb113-10" tabindex="-1"></a>                      (Ideology<span class="sc">|</span> Country_year), </span>
<span id="cb113-11"><a href="#cb113-11" tabindex="-1"></a>                    <span class="at">data =</span> Latino_analysis)</span></code></pre></div>
<div class="sourceCode" id="cb114"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb114-1"><a href="#cb114-1" tabindex="-1"></a><span class="fu">summary</span>(Latino_hyp3a)</span></code></pre></div>
<pre><code>## Linear mixed model fit by REML [&#39;lmerMod&#39;]
## Formula: Satisfaction ~ quota_effect * Ideology + Female + Education +  
##     Age + Polity_score + GDP_capita_logged + women_rep_lag +  
##     Corruption + Presidentialism + (Ideology | Country_year)
##    Data: Latino_analysis
## 
## REML criterion at convergence: 51345.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9704 -0.6333 -0.0909  0.6863  3.3230 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev. Corr 
##  Country_year (Intercept) 0.01872  0.1368        
##               Ideology    0.02123  0.1457   -0.59
##  Residual                 0.06994  0.2645        
## Number of obs: 274148, groups:  Country_year, 316
## 
## Fixed effects:
##                                      Estimate Std. Error t value
## (Intercept)                         2.356e-01  1.732e-01   1.361
## quota_effectNon-Effective          -5.148e-02  2.178e-02  -2.364
## quota_effectEffective              -7.879e-02  2.085e-02  -3.778
## Ideology                            2.249e-02  1.204e-02   1.867
## FemaleWoman                        -6.827e-03  1.013e-03  -6.738
## Education                          -1.759e-04  5.239e-05  -3.358
## Age                                 1.321e-04  3.206e-05   4.122
## Polity_score                        1.062e-02  4.812e-03   2.206
## GDP_capita_logged                   2.233e-02  1.617e-02   1.381
## women_rep_lag                       2.148e-04  9.248e-04   0.232
## Corruption                         -2.423e-01  4.551e-02  -5.324
## Presidentialism                     2.420e-01  4.779e-02   5.063
## quota_effectNon-Effective:Ideology  4.480e-02  2.227e-02   2.012
## quota_effectEffective:Ideology      2.395e-02  1.948e-02   1.230</code></pre>
<pre><code>## 
## Correlation matrix not shown by default, as p = 14 &gt; 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it</code></pre>
<div class="sourceCode" id="cb117"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb117-1"><a href="#cb117-1" tabindex="-1"></a><span class="fu">saveRDS</span>(Latino_hyp3a, <span class="st">&quot;Latino_hyp3a.rds&quot;</span>)</span></code></pre></div>
</div>
<div id="hypothesis-4-corruption-3" class="section level4">
<h4>Hypothesis 4-Corruption</h4>
<div class="sourceCode" id="cb118"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb118-1"><a href="#cb118-1" tabindex="-1"></a>Latino_hyp4 <span class="ot">&lt;-</span> <span class="fu">lmer</span>(Satisfaction <span class="sc">~</span> </span>
<span id="cb118-2"><a href="#cb118-2" tabindex="-1"></a>                      quota_effect<span class="sc">*</span>Corruption <span class="sc">+</span> </span>
<span id="cb118-3"><a href="#cb118-3" tabindex="-1"></a>                      Female <span class="sc">+</span></span>
<span id="cb118-4"><a href="#cb118-4" tabindex="-1"></a>                      Ideology <span class="sc">+</span> </span>
<span id="cb118-5"><a href="#cb118-5" tabindex="-1"></a>                      Education <span class="sc">+</span> </span>
<span id="cb118-6"><a href="#cb118-6" tabindex="-1"></a>                      Age <span class="sc">+</span> </span>
<span id="cb118-7"><a href="#cb118-7" tabindex="-1"></a>                      Polity_score <span class="sc">+</span> </span>
<span id="cb118-8"><a href="#cb118-8" tabindex="-1"></a>                      GDP_capita_logged <span class="sc">+</span></span>
<span id="cb118-9"><a href="#cb118-9" tabindex="-1"></a>                      women_rep_lag <span class="sc">+</span></span>
<span id="cb118-10"><a href="#cb118-10" tabindex="-1"></a>                      Presidentialism<span class="sc">+</span></span>
<span id="cb118-11"><a href="#cb118-11" tabindex="-1"></a>                      (<span class="dv">1</span> <span class="sc">|</span> Country_year),</span>
<span id="cb118-12"><a href="#cb118-12" tabindex="-1"></a>                      <span class="at">data =</span> Latino_analysis)</span></code></pre></div>
<div class="sourceCode" id="cb119"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb119-1"><a href="#cb119-1" tabindex="-1"></a><span class="fu">summary</span>(Latino_hyp4)</span></code></pre></div>
<pre><code>## Linear mixed model fit by REML [&#39;lmerMod&#39;]
## Formula: Satisfaction ~ quota_effect * Corruption + Female + Ideology +  
##     Education + Age + Polity_score + GDP_capita_logged + women_rep_lag +  
##     Presidentialism + (1 | Country_year)
##    Data: Latino_analysis
## 
## REML criterion at convergence: 56956.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9278 -0.6393 -0.0976  0.6935  3.2277 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  Country_year (Intercept) 0.01168  0.1081  
##  Residual                 0.07163  0.2676  
## Number of obs: 274148, groups:  Country_year, 316
## 
## Fixed effects:
##                                        Estimate Std. Error t value
## (Intercept)                           7.840e-02  1.753e-01   0.447
## quota_effectNon-Effective            -5.200e-02  7.336e-02  -0.709
## quota_effectEffective                 8.656e-02  4.269e-02   2.028
## Corruption                           -2.103e-01  4.628e-02  -4.544
## FemaleWoman                          -7.172e-03  1.024e-03  -7.001
## Ideology                              3.291e-02  1.957e-03  16.819
## Education                            -2.068e-04  5.295e-05  -3.906
## Age                                   1.890e-04  3.235e-05   5.841
## Polity_score                          1.050e-02  4.755e-03   2.208
## GDP_capita_logged                     3.845e-02  1.647e-02   2.334
## women_rep_lag                        -1.375e-03  1.005e-03  -1.369
## Presidentialism                       2.776e-01  4.823e-02   5.756
## quota_effectNon-Effective:Corruption  2.520e-02  1.030e-01   0.245
## quota_effectEffective:Corruption     -2.474e-01  6.290e-02  -3.934</code></pre>
<pre><code>## 
## Correlation matrix not shown by default, as p = 14 &gt; 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it</code></pre>
<div class="sourceCode" id="cb122"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb122-1"><a href="#cb122-1" tabindex="-1"></a><span class="fu">saveRDS</span>(Latino_hyp4, <span class="st">&quot;Latino_hyp4.rds&quot;</span>)</span></code></pre></div>
</div>
</div>
</div>
<div id="figures" class="section level2">
<h2>Figures</h2>
<div id="hypothesis-1a-and-1b-figures" class="section level3">
<h3>Hypothesis 1a and 1b figures</h3>
<div class="sourceCode" id="cb123"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb123-1"><a href="#cb123-1" tabindex="-1"></a><span class="co">#bring in the loaded models </span></span>
<span id="cb123-2"><a href="#cb123-2" tabindex="-1"></a>Americas_hyp1 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Americas_hyp1.rds&quot;</span>)</span>
<span id="cb123-3"><a href="#cb123-3" tabindex="-1"></a>CSES_hyp1 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;CSES_hyp1.rds&quot;</span>)</span>
<span id="cb123-4"><a href="#cb123-4" tabindex="-1"></a>Euro_hyp1 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Euro_hyp1.rds&quot;</span>)</span>
<span id="cb123-5"><a href="#cb123-5" tabindex="-1"></a>Latino_hyp1 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Latino_hyp1.rds&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb124"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb124-1"><a href="#cb124-1" tabindex="-1"></a><span class="fu">library</span>(dplyr)</span>
<span id="cb124-2"><a href="#cb124-2" tabindex="-1"></a></span>
<span id="cb124-3"><a href="#cb124-3" tabindex="-1"></a></span>
<span id="cb124-4"><a href="#cb124-4" tabindex="-1"></a></span>
<span id="cb124-5"><a href="#cb124-5" tabindex="-1"></a>Americas_hyp1_fig <span class="ot">&lt;-</span> sjPlot<span class="sc">::</span><span class="fu">plot_model</span>(Americas_hyp1,</span>
<span id="cb124-6"><a href="#cb124-6" tabindex="-1"></a>                                 <span class="at">type =</span> <span class="st">&quot;pred&quot;</span>, </span>
<span id="cb124-7"><a href="#cb124-7" tabindex="-1"></a>                                 <span class="at">terms =</span> <span class="st">&quot;quota_effect&quot;</span>)</span>
<span id="cb124-8"><a href="#cb124-8" tabindex="-1"></a></span>
<span id="cb124-9"><a href="#cb124-9" tabindex="-1"></a></span>
<span id="cb124-10"><a href="#cb124-10" tabindex="-1"></a>Americas_hyp1_fig_data <span class="ot">&lt;-</span> Americas_hyp1_fig<span class="sc">$</span>data</span>
<span id="cb124-11"><a href="#cb124-11" tabindex="-1"></a></span>
<span id="cb124-12"><a href="#cb124-12" tabindex="-1"></a></span>
<span id="cb124-13"><a href="#cb124-13" tabindex="-1"></a></span>
<span id="cb124-14"><a href="#cb124-14" tabindex="-1"></a>Americas_hyp1_fig_data <span class="ot">&lt;-</span> Americas_hyp1_fig_data <span class="sc">%&gt;%</span></span>
<span id="cb124-15"><a href="#cb124-15" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">Quota =</span> <span class="fu">case_when</span>(</span>
<span id="cb124-16"><a href="#cb124-16" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;No quota&quot;</span>,</span>
<span id="cb124-17"><a href="#cb124-17" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Non-effective quota&quot;</span>,</span>
<span id="cb124-18"><a href="#cb124-18" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Effective quota&quot;</span>),</span>
<span id="cb124-19"><a href="#cb124-19" tabindex="-1"></a>            <span class="at">Upper =</span> predicted <span class="sc">+</span> <span class="fl">1.41</span><span class="sc">*</span>std.error, <span class="co">#1.41 aligns with the 84% level </span></span>
<span id="cb124-20"><a href="#cb124-20" tabindex="-1"></a>            <span class="at">Lower =</span> predicted <span class="sc">-</span> <span class="fl">1.41</span><span class="sc">*</span>std.error, <span class="co"># which corresponds to the Bonferonni correction</span></span>
<span id="cb124-21"><a href="#cb124-21" tabindex="-1"></a>            <span class="at">Data =</span> <span class="st">&quot;AmericasBarometer&quot;</span>)</span>
<span id="cb124-22"><a href="#cb124-22" tabindex="-1"></a></span>
<span id="cb124-23"><a href="#cb124-23" tabindex="-1"></a></span>
<span id="cb124-24"><a href="#cb124-24" tabindex="-1"></a></span>
<span id="cb124-25"><a href="#cb124-25" tabindex="-1"></a></span>
<span id="cb124-26"><a href="#cb124-26" tabindex="-1"></a>CSES_hyp1_fig <span class="ot">&lt;-</span> sjPlot<span class="sc">::</span><span class="fu">plot_model</span>(CSES_hyp1,</span>
<span id="cb124-27"><a href="#cb124-27" tabindex="-1"></a>                                 <span class="at">type =</span> <span class="st">&quot;pred&quot;</span>, </span>
<span id="cb124-28"><a href="#cb124-28" tabindex="-1"></a>                                 <span class="at">terms =</span> <span class="st">&quot;quota_effect&quot;</span>)</span>
<span id="cb124-29"><a href="#cb124-29" tabindex="-1"></a></span>
<span id="cb124-30"><a href="#cb124-30" tabindex="-1"></a></span>
<span id="cb124-31"><a href="#cb124-31" tabindex="-1"></a></span>
<span id="cb124-32"><a href="#cb124-32" tabindex="-1"></a>CSES_hyp1_fig_data <span class="ot">&lt;-</span> CSES_hyp1_fig<span class="sc">$</span>data</span>
<span id="cb124-33"><a href="#cb124-33" tabindex="-1"></a></span>
<span id="cb124-34"><a href="#cb124-34" tabindex="-1"></a></span>
<span id="cb124-35"><a href="#cb124-35" tabindex="-1"></a></span>
<span id="cb124-36"><a href="#cb124-36" tabindex="-1"></a>CSES_hyp1_fig_data <span class="ot">&lt;-</span> CSES_hyp1_fig_data <span class="sc">%&gt;%</span></span>
<span id="cb124-37"><a href="#cb124-37" tabindex="-1"></a>  <span class="fu">mutate</span>( <span class="at">Quota =</span> <span class="fu">case_when</span>(</span>
<span id="cb124-38"><a href="#cb124-38" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;No quota&quot;</span>,</span>
<span id="cb124-39"><a href="#cb124-39" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Non-effective quota&quot;</span>,</span>
<span id="cb124-40"><a href="#cb124-40" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Effective quota&quot;</span>),</span>
<span id="cb124-41"><a href="#cb124-41" tabindex="-1"></a>            <span class="at">Upper =</span> predicted <span class="sc">+</span> <span class="fl">1.41</span><span class="sc">*</span>std.error,</span>
<span id="cb124-42"><a href="#cb124-42" tabindex="-1"></a>            <span class="at">Lower =</span> predicted <span class="sc">-</span> <span class="fl">1.41</span><span class="sc">*</span>std.error, </span>
<span id="cb124-43"><a href="#cb124-43" tabindex="-1"></a>            <span class="at">Data =</span> <span class="st">&quot;CSES&quot;</span>)</span>
<span id="cb124-44"><a href="#cb124-44" tabindex="-1"></a></span>
<span id="cb124-45"><a href="#cb124-45" tabindex="-1"></a></span>
<span id="cb124-46"><a href="#cb124-46" tabindex="-1"></a>Euro_hyp1_fig <span class="ot">&lt;-</span> sjPlot<span class="sc">::</span><span class="fu">plot_model</span>(Euro_hyp1,</span>
<span id="cb124-47"><a href="#cb124-47" tabindex="-1"></a>                                 <span class="at">type =</span> <span class="st">&quot;pred&quot;</span>, </span>
<span id="cb124-48"><a href="#cb124-48" tabindex="-1"></a>                                 <span class="at">terms =</span> <span class="st">&quot;quota_effect&quot;</span>)</span>
<span id="cb124-49"><a href="#cb124-49" tabindex="-1"></a></span>
<span id="cb124-50"><a href="#cb124-50" tabindex="-1"></a></span>
<span id="cb124-51"><a href="#cb124-51" tabindex="-1"></a></span>
<span id="cb124-52"><a href="#cb124-52" tabindex="-1"></a>Euro_hyp1_fig_data <span class="ot">&lt;-</span> Euro_hyp1_fig<span class="sc">$</span>data</span>
<span id="cb124-53"><a href="#cb124-53" tabindex="-1"></a></span>
<span id="cb124-54"><a href="#cb124-54" tabindex="-1"></a></span>
<span id="cb124-55"><a href="#cb124-55" tabindex="-1"></a></span>
<span id="cb124-56"><a href="#cb124-56" tabindex="-1"></a>Euro_hyp1_fig_data <span class="ot">&lt;-</span> Euro_hyp1_fig_data <span class="sc">%&gt;%</span></span>
<span id="cb124-57"><a href="#cb124-57" tabindex="-1"></a>  <span class="fu">mutate</span>( <span class="at">Quota =</span> <span class="fu">case_when</span>(</span>
<span id="cb124-58"><a href="#cb124-58" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;No quota&quot;</span>,</span>
<span id="cb124-59"><a href="#cb124-59" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Non-effective quota&quot;</span>,</span>
<span id="cb124-60"><a href="#cb124-60" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Effective quota&quot;</span>),</span>
<span id="cb124-61"><a href="#cb124-61" tabindex="-1"></a>            <span class="at">Upper =</span> predicted <span class="sc">+</span> <span class="fl">1.41</span><span class="sc">*</span>std.error,</span>
<span id="cb124-62"><a href="#cb124-62" tabindex="-1"></a>            <span class="at">Lower =</span> predicted <span class="sc">-</span> <span class="fl">1.41</span><span class="sc">*</span>std.error, </span>
<span id="cb124-63"><a href="#cb124-63" tabindex="-1"></a>            <span class="at">Data =</span> <span class="st">&quot;Eurobarometer&quot;</span>)</span>
<span id="cb124-64"><a href="#cb124-64" tabindex="-1"></a></span>
<span id="cb124-65"><a href="#cb124-65" tabindex="-1"></a></span>
<span id="cb124-66"><a href="#cb124-66" tabindex="-1"></a>Latino_hyp1_fig <span class="ot">&lt;-</span> sjPlot<span class="sc">::</span><span class="fu">plot_model</span>(Latino_hyp1,</span>
<span id="cb124-67"><a href="#cb124-67" tabindex="-1"></a>                                 <span class="at">type =</span> <span class="st">&quot;pred&quot;</span>, </span>
<span id="cb124-68"><a href="#cb124-68" tabindex="-1"></a>                                 <span class="at">terms =</span> <span class="st">&quot;quota_effect&quot;</span>)</span>
<span id="cb124-69"><a href="#cb124-69" tabindex="-1"></a></span>
<span id="cb124-70"><a href="#cb124-70" tabindex="-1"></a></span>
<span id="cb124-71"><a href="#cb124-71" tabindex="-1"></a></span>
<span id="cb124-72"><a href="#cb124-72" tabindex="-1"></a>Latino_hyp1_fig_data <span class="ot">&lt;-</span> Latino_hyp1_fig<span class="sc">$</span>data</span>
<span id="cb124-73"><a href="#cb124-73" tabindex="-1"></a></span>
<span id="cb124-74"><a href="#cb124-74" tabindex="-1"></a></span>
<span id="cb124-75"><a href="#cb124-75" tabindex="-1"></a></span>
<span id="cb124-76"><a href="#cb124-76" tabindex="-1"></a>Latino_hyp1_fig_data <span class="ot">&lt;-</span> Latino_hyp1_fig_data <span class="sc">%&gt;%</span></span>
<span id="cb124-77"><a href="#cb124-77" tabindex="-1"></a>  <span class="fu">mutate</span>( <span class="at">Quota =</span> <span class="fu">case_when</span>(</span>
<span id="cb124-78"><a href="#cb124-78" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;No quota&quot;</span>,</span>
<span id="cb124-79"><a href="#cb124-79" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Non-effective quota&quot;</span>,</span>
<span id="cb124-80"><a href="#cb124-80" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Effective quota&quot;</span>),</span>
<span id="cb124-81"><a href="#cb124-81" tabindex="-1"></a>            <span class="at">Upper =</span> predicted <span class="sc">+</span> <span class="fl">1.41</span><span class="sc">*</span>std.error,</span>
<span id="cb124-82"><a href="#cb124-82" tabindex="-1"></a>            <span class="at">Lower =</span> predicted <span class="sc">-</span> <span class="fl">1.41</span><span class="sc">*</span>std.error, </span>
<span id="cb124-83"><a href="#cb124-83" tabindex="-1"></a>            <span class="at">Data =</span> <span class="st">&quot;Latinobarometer&quot;</span>)</span>
<span id="cb124-84"><a href="#cb124-84" tabindex="-1"></a></span>
<span id="cb124-85"><a href="#cb124-85" tabindex="-1"></a></span>
<span id="cb124-86"><a href="#cb124-86" tabindex="-1"></a>hyp1_fig_data <span class="ot">&lt;-</span> <span class="fu">rbind</span>(Americas_hyp1_fig_data,</span>
<span id="cb124-87"><a href="#cb124-87" tabindex="-1"></a>      CSES_hyp1_fig_data,</span>
<span id="cb124-88"><a href="#cb124-88" tabindex="-1"></a>      Euro_hyp1_fig_data,</span>
<span id="cb124-89"><a href="#cb124-89" tabindex="-1"></a>      Latino_hyp1_fig_data)</span>
<span id="cb124-90"><a href="#cb124-90" tabindex="-1"></a></span>
<span id="cb124-91"><a href="#cb124-91" tabindex="-1"></a>hyp1_fig_data<span class="sc">$</span>Quota <span class="ot">&lt;-</span> <span class="fu">as.factor</span>(hyp1_fig_data<span class="sc">$</span>Quota)</span>
<span id="cb124-92"><a href="#cb124-92" tabindex="-1"></a></span>
<span id="cb124-93"><a href="#cb124-93" tabindex="-1"></a>hyp1_fig_data<span class="sc">$</span>Quota <span class="ot">&lt;-</span> <span class="fu">factor</span>(hyp1_fig_data<span class="sc">$</span>Quota , <span class="at">levels =</span> <span class="fu">c</span>(<span class="st">&quot;No quota&quot;</span>, <span class="st">&quot;Non-effective quota&quot;</span>, <span class="st">&quot;Effective quota&quot;</span>))</span>
<span id="cb124-94"><a href="#cb124-94" tabindex="-1"></a></span>
<span id="cb124-95"><a href="#cb124-95" tabindex="-1"></a></span>
<span id="cb124-96"><a href="#cb124-96" tabindex="-1"></a>hyp1_fig_data <span class="ot">&lt;-</span> <span class="fu">na.omit</span>(hyp1_fig_data)</span></code></pre></div>
<div class="sourceCode" id="cb125"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb125-1"><a href="#cb125-1" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb125-2"><a href="#cb125-2" tabindex="-1"></a><span class="fu">ggplot</span>(hyp1_fig_data, <span class="fu">aes</span>(<span class="at">x=</span>Quota, <span class="at">y=</span>predicted, <span class="at">fill =</span> Quota)) <span class="sc">+</span> </span>
<span id="cb125-3"><a href="#cb125-3" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">stat =</span> <span class="st">&quot;identity&quot;</span>,<span class="at">position =</span> <span class="st">&quot;dodge&quot;</span>, <span class="at">width=</span>.<span class="dv">9</span>) <span class="sc">+</span> </span>
<span id="cb125-4"><a href="#cb125-4" tabindex="-1"></a>      <span class="fu">scale_x_discrete</span>(<span class="at">limits=</span>rev) <span class="sc">+</span></span>
<span id="cb125-5"><a href="#cb125-5" tabindex="-1"></a></span>
<span id="cb125-6"><a href="#cb125-6" tabindex="-1"></a>  <span class="fu">ylim</span>(<span class="dv">0</span>,<span class="fl">0.65</span>) <span class="sc">+</span></span>
<span id="cb125-7"><a href="#cb125-7" tabindex="-1"></a>       <span class="fu">geom_text</span>(<span class="fu">aes</span>(<span class="at">label=</span><span class="fu">sprintf</span>(<span class="st">&quot;%0.3f&quot;</span>, predicted)), <span class="at">position=</span><span class="fu">position_dodge</span>(<span class="at">width=</span><span class="fl">0.9</span>), <span class="at">hjust =</span> <span class="fl">2.5</span>, <span class="at">vjust=</span><span class="dv">0</span>, </span>
<span id="cb125-8"><a href="#cb125-8" tabindex="-1"></a>                 <span class="at">size=</span><span class="fl">3.8</span>,</span>
<span id="cb125-9"><a href="#cb125-9" tabindex="-1"></a>                 <span class="at">colour =</span> <span class="st">&quot;white&quot;</span>, <span class="at">fontface =</span> <span class="st">&quot;bold&quot;</span>) <span class="sc">+</span> </span>
<span id="cb125-10"><a href="#cb125-10" tabindex="-1"></a>  </span>
<span id="cb125-11"><a href="#cb125-11" tabindex="-1"></a>   <span class="fu">geom_text</span>(<span class="fu">aes</span>(<span class="at">label=</span><span class="fu">sprintf</span>(<span class="st">&quot;(%0.3f, %0.3f)&quot;</span>, Lower, Upper)), <span class="at">position=</span><span class="fu">position_dodge</span>(<span class="at">width=</span><span class="fl">0.9</span>), <span class="at">hjust =</span> <span class="fl">1.6</span>, <span class="at">vjust =</span> <span class="dv">2</span>, </span>
<span id="cb125-12"><a href="#cb125-12" tabindex="-1"></a>                 <span class="at">size=</span><span class="fl">2.8</span>, </span>
<span id="cb125-13"><a href="#cb125-13" tabindex="-1"></a>               <span class="at">colour =</span> <span class="st">&quot;white&quot;</span>, <span class="at">fontface =</span> <span class="st">&quot;bold&quot;</span>) <span class="sc">+</span> </span>
<span id="cb125-14"><a href="#cb125-14" tabindex="-1"></a></span>
<span id="cb125-15"><a href="#cb125-15" tabindex="-1"></a>  </span>
<span id="cb125-16"><a href="#cb125-16" tabindex="-1"></a>  <span class="fu">geom_errorbar</span>(<span class="fu">aes</span>(<span class="at">ymin=</span>Lower, <span class="at">ymax=</span>Upper), <span class="at">width=</span><span class="dv">0</span>,</span>
<span id="cb125-17"><a href="#cb125-17" tabindex="-1"></a>                 <span class="at">position=</span><span class="fu">position_dodge</span>(.<span class="dv">9</span>)) <span class="sc">+</span> </span>
<span id="cb125-18"><a href="#cb125-18" tabindex="-1"></a>    <span class="fu">scale_fill_grey</span>(<span class="at">start =</span> <span class="fl">0.75</span>, <span class="at">end =</span> .<span class="dv">35</span>,</span>
<span id="cb125-19"><a href="#cb125-19" tabindex="-1"></a>                   <span class="at">guide =</span> <span class="fu">guide_legend</span>(<span class="at">reverse =</span> <span class="cn">FALSE</span>))  <span class="sc">+</span></span>
<span id="cb125-20"><a href="#cb125-20" tabindex="-1"></a></span>
<span id="cb125-21"><a href="#cb125-21" tabindex="-1"></a></span>
<span id="cb125-22"><a href="#cb125-22" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">y =</span> <span class="st">&quot;Predicted Satisfaction with Democracy&quot;</span>, </span>
<span id="cb125-23"><a href="#cb125-23" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Quota type&quot;</span>,</span>
<span id="cb125-24"><a href="#cb125-24" tabindex="-1"></a>       <span class="at">title =</span> <span class="st">&quot;&quot;</span>) <span class="sc">+</span></span>
<span id="cb125-25"><a href="#cb125-25" tabindex="-1"></a>    <span class="fu">theme_bw</span>(<span class="dv">12</span>) <span class="sc">+</span></span>
<span id="cb125-26"><a href="#cb125-26" tabindex="-1"></a>    <span class="fu">facet_wrap</span>(<span class="sc">~</span>Data) <span class="sc">+</span> </span>
<span id="cb125-27"><a href="#cb125-27" tabindex="-1"></a></span>
<span id="cb125-28"><a href="#cb125-28" tabindex="-1"></a></span>
<span id="cb125-29"><a href="#cb125-29" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position=</span><span class="st">&quot;none&quot;</span>, <span class="at">legend.title =</span> <span class="fu">element_blank</span>()) <span class="sc">+</span> </span>
<span id="cb125-30"><a href="#cb125-30" tabindex="-1"></a>  <span class="fu">coord_flip</span>()</span></code></pre></div>
<pre><code>## Warning in geom_col(stat = &quot;identity&quot;, position = &quot;dodge&quot;, width = 0.9):
## Ignoring unknown parameters: `stat`</code></pre>
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zJ9fubKrfOBW7Lfq+1jVKJvHABKpvvq7Tu+a19MLa6sLN4KLqa9uerq9tt7hz+/mvaqbRm8/Y1c+2LqnuVJItLeG/rc6pWXbZ8OEZHE67SgqwCKIgMPAOJk/Lym5c/vtu/v19WtZJtw51ZL99Xbd0IXe9uN1d3399vbey+GvgnnuzCcVuzg7Yd3Qr3tibXc29t7v7xzO6Ub0zJ4+86Xve3tGdft1icWJscatAzevvNNqLfdOFhE9tvbe0PfPHRcy/b29t6LoTtpJ7ioQ/b7zXiM2uVuZ8yuL5zTr6xl8PaXFxNPLJGIL9E3DgCl0zJ49fadb0IXe9vbzVuEaC+vh7fTo3fTSdrrztR2JN54RXo9l+cqQJWo22eTHqDEOjs7RWRmZqbSFQEAlMnZs2dFJBKJOD+FxgIADpo8Ggsy8AAAAAAAKIAAHgAAAAAABRDAAwAAAACgAAJ4AAAAAAAUQAAPAAAAAIACCOABAAAAAFAAATwAAAAAAAoggAcAAAAAQAEE8AAAAAAAKIAAHgAAAAAABRDAAwAAAACgAAJ4AAAAAAAU8FqlKwAcFGfPnq10FQAA1Y7GAgCQBRl4AAAAAAAUQAAPAAAAAIACGEIPlEkkEsn73J2dHe0fDQ0NRapOVdjZ2amxOxK+LKXwZSlEuS+rs7MzvxNpLDLxP2mF8GUphC+rGuTRWJCBBwAAAABAAQTwAAAAAAAogAAeAAAAAAAFEMADAAAAAKAAAngAAAAAABRAAA8AAAAAgAII4AEAAAAAUAABPAAAAAAACiCABwAAAABAAQTwAAAAAAAogAAeAAAAAAAFEMADAAAAAKAAAngAAAAAABTwWqUrAMCRn3/+udJVAFALTp06VekqoIRoLABUD1qcUiADDwAAAACAAgjgAQAAAABQAAE8AAAAAAAKIIAHAAAAAEABBPAAAAAAACiAAB4AAAAAAAUQwAMAAAAAoAACeAAAAAAAFEAADwAAAACAAgjgAQAAAABQAAE8AAAAAAAKIIAHAAAAAEABBPAAAAAAACiAAB4AAAAAAAUQwAMAAAAAoAACeAAAAAAAFEAADwAAAACAAgjgAQAAAABQAAE8AAAAAAAKIIAHAAAAAEABBPAAAAAAACiAAB4AAAAAAAUQwAMAAAAAoAACeAAAAAAAFEAADwAAAACAAgjgAQAAAABQAAE8AAAAAAAKeK3SFQAAlJqn8UiH75DHo/1XPL69s/5rdCvuqozGI6c6Dtn/efvFw+dbDk7Ofpz19eLrT59G3dUWAFAZFW9xJHcLUmj5QCURwANATfP4jx3zeVI+8RzydBxqOPTixXPnXSqP5/W8q9B4JFtHqVinAAAqrApaHHHQghRYPlBZDKEHgBrWeCStL5XgOdTR4bf+k+Xhrzs/NvVE/zHXsbjH7yN8BwDFVL7FEXHUghRUPlBpBPAAULNyZCE8PscdqvzSFR7/kfRkjKOzOlyfAwCorEq3OPq5DloQEvBQG0PoAaBWNR4ydaaMaYCexiMdHYf07o2nodETdTK93JSucDQ30NPof9PnO5RHHE74DgAKqmCLYzrTUQuSf/lANSADDwA1ytybiq+/0HtN8a3n69uJzz0NjU6iZVO6Iv577t5X45FjHXlF74TvAKCkyrU4phOdtSB5lw9UBwJ4AKhN5jGC8R3z4kFb26b+lKOZgKajfo+76+7E15++2M59mKbxTb3zFd/eplcFAKqohhbHaQtSQIsGVAOG0ANAbTJ3lNL6KPHf4yL6X1/3eERy9GDM6QrxHDl2TN8gKNf2QPH1Fy+iW/HGI85qnJxAub3+6++sYwcAqqh8i+O4Bcm3RQOqBQE8ABw48fjvie6U53UH3alkz8xzyDRA0X57oPj2utt+kKnz9eL5lvh9Ls4FAFSp0rc44qoFyat8oIowhB4AalLKeMbCZvnlWLDXc6jj2JFG8ydbz58+d5nFSOt8AQDUUckWR9y1IPmUD1QVAngAQHYOZi0e6iiow5Pctje+/ivhOwAcWO5bHHctSOlbNKDEGEIPAMgqJV0R3zZGF3oa/R2mBX8PHWqUrfxi7+TCwcmliwEAB5DrFsdlC1LyFg0oOTLwEJHY/GhfX19fX9/4cs6DRudj5atYWcSWl2vtlgDRZx3qnC38a2trZ317Ox4Xke0XTxNzA+Nb0ZT15V/35HcRwncAUFzFWhz3LUhpWzSgDMjAwywcGu9eGOuqdDXKJrY8fj0UjgaCC13Nla4LUEbuZivG49Hoc4la/Wlre7vD2PvXydpEFlVJbtu7vU74DgC1poQtTh4tSElbNKAsCOCR6mCF8BtLYctXOFALsuzck2W/nzLzNDYkhyx2nDrVYXmQ79gpn4hsv3jI+nYAUIUq0uLQguBgIoBHuoMVwgO1K2XnnoZGTzSRnXCXDmk8csz3uoh4PB6Jrz99ak5yNBrJCkclAQBqkyotDi0aagBz4JHkDwT8IiLhULa58AAUsbWdnM/n8XX4Gz0i2ko9yYV64js5d3uL/y4ej0ebDujxdRxpNLpojf5jHabeTu6SAAA1SpEWhxYNNYAMPEwOD1wZ+nVkOuoyCx9bnp+beRSOaqPR/f7AybMD/c4nlaedHgheGetqXh7vC4XFPzQ50a8VpH0ggWB6vez+kKNW+mkiIhIO9YUltYjUs/O5LaDyzPP5xOPrOOZLH1+Y2kdpPHIq0X9JDjaMb+3EfYnlfDyHOo5ZDVPMcwJ7PPr0oeVEFo//2DGj15eeJQEAVJsKtDh5tCAlbdGA8iADjxTN/VeGXGXhY8vjoyOhaVOkG42Gp0Mjow6T+Jmnh0MjWVfDL32tYvOjfalnuywAqBZbz82L6mZwuGZvPPpiPcdh2y+YWggAB5oaLQ4tGtRHBh5pmvuvDD1ymoWPzV8PhaMi4h8KXtGy0/rC7tFwaPRwIn1ue/qofrqWdheR2PL89dB0OBQq4BYc1aprbGFhzCp7H5u/Pp1ap+T5Eg3PzA902d1VZ2dnlmrt7OwUcFNAfraeP/UkExEptl843rQtHn36VGyK0ffRLaCOKIFAIJDfidvb2Xrg1aDm36U1f4OoXWq0OLRoRZdHi1OGtqaG36Vk4JHBeRZ+eU4LdYcmJxJjy5u7xiYmh/wiEp2ec3z6WPL0/omFYJ4dz2LUKvbjI+3sZPSuna8/lOirjUIqB5RdPPr04dMX26aVf+Px7fUXTx8+dzXDTytm3VyOXtBTdwUBAGqVIi0OLRqURgYeFhxm4ZeXwiIigbPpGenm/rOB6VBYwkvLY122OXzb06VrYMgfns5zg7cCa9XcP7HQb/mHw2+K9b6hQNWLbz1/6iCjsPX8Ydaj4lvR51t5/L8gV7mW17Kb2ghHwuGw3Z/+9Kc/lbMmAA6YCrc4Is5akELKRypanDIjgIclJyF87NWvIiL+wy2Zf2s57BeJyq+vYmK37Fu205vfOemfjub1Vi2wVumlxWKysfHjq6VHj8K56xOJRCw/14bWNzQ0OLqmlRoeBQSgzAp5F1VQ4jWoaP2do7EAUDPK/8Y+CI0FATxs5A7hN15FRUTePGwRC+vp6uirDZHsobLl6QUoRq1iy/NzM9NhfpYFAAAAUEWYAw9bzf1ntbnoFdgXvvnwm2W+YkJseXwklIje/X6/PxAYCgYntSn0AAAAAFAhZOCRRddYMBAOhUXCM/MDV9L/mm1AeraB7Kmsx7Pr5zuQfmShtVqe07aHDwQnx1IKiL1yWCMAAAAAKAUy8Miqa0xbET46fX0uPYDVs+SWy7JnG8hunP7OSb+IRB/9GLM9P9OvryyOLmKtjCXwgmPp4b9tlQAAAACgHAjgkUMihA9nzAnv6g6IiIRn5tOC6tj8jBYGd2fbRt6I4DP3dTPON2s5bLmLm75nXPFqZcOqSgAAAABQRgTwyMkI4S3+MqBvrT4yOr+sh8ux5fHREX0j9oHskbIxyT4cGh2fj2Wcn3awPis+HBo3XyqUGVa7r5Upra//TmC6isTsqgQAAAAAZcQceDiQmAufrrn/SvDV9VA4Gp0OjUyb/+IPBK9k7O9uUfBkUK6HwtHw9EjYdL7f75do2rZtyQn5oZFkVQLBoITSqua8VvqE+ej0SN+0SCC4MNaVXHx/JKVU/1Dw5KPQtKtN6AAAAACgeMjAwxHbLHxz19jEZHAo4E8s0e73B4aCkxMZc8itJc43nR6cnLhy0rISqUcGhoKT1lvUO61Vc/9EcChxkJaIb+6fmAxmnLkw0d+lz663nLQPAAAAACVWt7+/X+k6ABli86Mj01HxD01O5M7jV73Ozk4RiUQieZews7Pz888/F69GAA6uU6dOVboK+djZ2dH+0dDQUNmaOJTHm5/GAkCNKX+LcxAaCzLwAAAAAAAogAAeAAAAAAAFEMADAAAAAKAAAngAAAAAABTANnKoSs39Ewv9la4EAAAAAFQRMvAAAAAAACiADDwAVEjjkVMdh0S2Xzx8vmV85mk80uE75PGIiEh8e3391+hWPEc5Hv+xYz5Pxsfx9adPo8lzXZRsFJhWgnOluguPp/HNZMESj2/vmIq2KcGgPWftoed9ZwCgoLI2N6kFx7fXXzzPVrBetzzfyyVrbrIeQ3ODiiIDDwAV0XhE77L8muxO+Y8d60iEpyKeQ76OY8f89p0E/bDXcx3hruTGN7P0S3Ir1V14/MdSCxaPw6LNtn5dj4t4fB2uzgIAdZWzucko2HOo49ipI4056paf0jWajo7JgeYGpUIGHgAqwOP3HRIRie9sJX/v79AC5+31p8+Tv/B7fG82RpM5k0yNh7Tej/3P/C5K9jQeOVZAb6p0d5Eo1yhYPI3+jg6fRzy+Dv/W02hc4tGnD6PpZxlZkkTPNb61E/d5PDkrBAA1oZzNTaLgtEz1oY4jjVsWJRcYvpeq0cx5DM0NKooMPACUn6exQWvnTR0q46P1X7XeQjy6vi0iIod82X6+93hel7Si7C6WvWRPo//IscKi99LdhcfzusTj5oIlvmUU7WlotCzaFPS/SHbB4ls78dwVAoCaUM7mxuOR3+PxuMj2elrB8rons+CCwvcSNprOjkk9geYGZUQGHgDKLkuHSn6PJ7oL8d/jIh59KJ9dJ8IY52c6z/pi2Us2Zd7jcbHoabm6saLfRXzr+VP7/IVl0eb+VEruQ0+KiKeh0RNlaiKAWlbW5ia+FX2+lZKZNiJhC4WF7yW8C4fHmA+nuUFZEcADQLlZ9Tus5tvF47+LeERPXVg3/cY4P3ndd+xUh9ZPS10zyFXJ8e31F8/jb57Ks19VurvIelb898zDEjP5zdM+U2tElwpAjStvc5N59cSLOD2VbYTv8fX1332+PFqcUt6FyzuluUGZMYQeAMrN6HdYxZ1Wn2VZTSeZ3fAksuYez6GOY8cy1gzKVXJ8e/3FU216ecFKdxfJc/zJIQOZgxyNSZ8SX3+ReUdG9YqwShEAVLHKNDci4vEfO3VKe0vHtzMmkifDd4s3tDsluAu3d0pzgzIjAw8AZZboGzgcnJe1rNe1Ee/x7RdabsDTeKSj45BHPHZrBtnIOkS91FzfRcoWPsZkS/PfjaST9QRGJ1kaAFBeVTQ3nkMNbzZumbLXqeG77c+0JeHkLlzdKc0Nyo8AHgDKrJg/xGfE3fGt5+uHtBHwhw41ypYSC9+6u4vUDXjTZhyKiGk8o0VsnyLrREkAUFzlmht9nXZtZxPPoY5jR0Tfg76I2fc8OLkLN3dKc4MKYAg9AFQTy+6W5RBBW1vblqv+FqFkx0p3F41HUqP3h1YbExnTF7e3lfj9AgAqoHQvalNx2mbokliJvejheznuwv4YmhtUAhl4AKgGxuK5ZnkOfkxb9beIJedUursQEX2pfKP4+PaLp5ZjNhNn0qECgAwlflGnX80YQ+553SPiMQJej+/YKV9qWb5jp3xZN2dPLbesd2FzDM0NKoIAHgDKzKrbYTlPLtvqQ5rGI8d8r4vH4zGnohP5CK0Lk1/J+SndXeir1iWid/s+XnJCYu6bK9EABACoCmVuboxjxPSGTsS4lnXJX+nvInuTJEJzg0ohgPJubRgAACAASURBVAeAMrNe02br13XfIZ9HPL4O/9bTaDy5sq1pZl3jEW0WnhG/bm3/3qFNy+s40hh/vhUXT2Jr3cR+No5KdiL96hZKdxeJiYZZry823ay0Q0o0AAEAqkqZm5v47+I55BERX4c//iK6FTftkK6t8RZ//jA9VW3ZslSyuXHUJIkIzQ0qhQAeAMrNyEOkrmkTj75Ybzjm86SNLMzcV9Zs6/kLY2WdjmOnOpJ/2H6RnF+YV8l5KtFdJDpmIlZDL015ktRsj7VSDUAAgOpS3uYmUa54fB3HfMlj4tsvnA2Md6NkjaajhlVoblAxLGIHAOUW39rRWvK0BXPi0adPX2wnf6XXdmbP0enZev4w5SSJx7dfPH34PGU/m7xKzlMp7iIxTtGF3BkR601/AKBmlLu5iUefph+zvf7iqfWCJQUrWaPpqGFNorlBedXt7+9Xug5Ajevs7BSRSCSSdwk7Ozs///xz8WqEijN2QnO8XE/VaDxyqkMs135XhroPvzhOnTpV6SrkY2dnR/tHQ0NDZWviUB5vfhoLlIC6bzyam1pQ/hbnIDQWZOABoPyMpIinobF4S/qUnqfxiO+Q8qvt6iMaSYgAOABobiqI5gYlQQAPABUQj65vi6jVpfL4Ozpe/91m7zZlGLPpXS7gBwBqormpFJoblAgBPABUxNav63FRqksVjz59+NRuCqAq9Nn0JVnADwCqEc1NRdDcoFRYhR4AKiMeffowWulKHDg8dQAHDi++SuCpo1TIwAMAAAAAoAACeAAAAAAAFEAADwAAAACAAgjgAQAAAABQAAE8AAAAAAAKIIAHAAAAAEABBPAAAAAAACiAAB4AAAAAAAUQwAMAAAAAoAACeAAAAAAAFPBapSsAwJE//elPItLQ0FDpihTTzs5Ojd2RiOzs7Gj/qLFb48tSSE1+WXCIxkIVvH8UwpeFqkIGHgAAAAAABRDAAwAAAACgAAJ4AAAAAAAUQAAPAAAAAIACCOABAAAAAFAAATwAAAAAAAoggAcAAAAAQAEE8AAAAAAAKIAAHgAAAAAABbxW6QoAcGR+fr7SVQAAGR4ernQVkA2NBYCaROuTQAYeAAAAAAAFEMADAAAAAKAAAngAAAAAABRAAA8AAAAAgAII4AEAAAAAUAABPAAAAAAACiCABwAAAABAAQTwAAAAAAAogAAeAAAAAAAFEMADAAAAAKAAAngAAAAAABRAAA8AAAAAgAII4AEAAAAAUAABPAAAAAAACiCABwAAAABAAQTwAAAAAAAogAAeAAAAAAAFEMADAAAAAKAAAngAAAAAABRAAA8AAAAAgAII4AEAAAAAUAABPAAAAAAACiCABwAAAABAAQTwAAAAAAAogAAeAAAAAAAFEMADAAAAAKAAAngAAAAAABTwWqUrAACocvVtp997u9Xr1f5rb28t+lPkyequqzLaTg/3tNr/ee3B1P1Vh+fvPZu7+yTX1d0eDwCoNpVpferbjne+7U9cVvb29vYcXjh5sZytGpA3AngAgL3642cGjnrNn3i9rd6eVn/rg+/uO+9G1dd7cx9kr+10tv5X4ccDAKpLZVqfzKuKeL1er6ML0/SgPBhCDwCw03Y6oyej87b2vHe83nFB9X8oIICvP/62qz6R2+MBANWlMq2P/VVzX7j++BnCd5QHATwAwFqOZIL3qONOVCEJ+Prj79l3qIpwPACgulSm9Un58Xfv2YO5qampqam5B2t7yQt3tlmfeTozcQ+UCkPoAQCW2lpNHShjInl92+n3elr1borX31b/xMn0clMKxOW8QMJ3ADhgKtT6mI7de/adMeV9d/X+T62JafStrW2yaiqlvu1459tHW2l2UE5k4AEAVsw9qL1n3z1JdmXWEp97/W1OsiCmFMjeP90sP0T4DgAHTRW0Pnu75oN3/7lnfVjb6YEeoneUHQE8AMCCedzhXtS8bs/qmqkP9QdHXShTWmPXRReqrVMPx/fW1my6TwUdDwCoNhVrfUxxurfeXLq5FNufAfaezT1Ys/kbUFQMoQcAWDAv/JPW79n9556IMY6xvl4kR6/InAKR+tNnzui78+TYEig5CXLtp8g/c69L5/Z4AED1qVjrs7sa3TuqHeA9+t7xXW0Ufb15Qv7aT5bj9veePfjuyepu22lHNwgUiAAeAODO7q6pC/UHB12oZG/M22oa4p51SyBTOP7g/qocfztHpdweDwBQTIlbn90nd+dEX43Oe7Rn4GhPSml7z+YyJtHvrT1zvzU9UBiG0AMAMqWMYXQ1bz1rURa8rT0Dp9PX9U0Lx3NyezwAoCpVtvXZfXJ37pnVHCxjLb0Uq/fv3id6R9kRwAMASsrBLrytPSmdqORePnvPIg7CcbfHAwBqXz6tz5nhActl6bxHB4bPuNh/HigdhtADAEopJQWyt2aMWKxvO/5eT3JEo2lrnuRS8snlh7NewOXxAIADwHXr03batJn72oO5+9oc+MTh3qMDZ8QiEQ+UlxIZ+Nj8aF9fX19f3/hyzoNG52Plq1hBYsvz4/p9Jett+WEprr2cUvLyuFrPrrjSnwYA0Wca6pwt9mtrNfpsbW1vT0TWHtxNzDfcXX1y17xir7HmL+E7ABxgFWx9kouf7j2bu5/YB958uPdoZ/qUL6Dc1MrAh0Pj3QtjXZWuRjEsj4+Ewqb/fvNws82HRRZbHr8eCkcDwYWu4heuHJ4GkA93MxR3d588uS9PrP60urbWY+z3q61HZNrI3Wax37SquDweAKCsErY+qZvFRVcLWv0eKCm1AvjaCeGXl7RAPRCcHEuEjpYfFtvGUjia8WHX2MLCWIkuWNUsnwYAkaz9lSx7/BSovs2fHNbYMzzcY3WQ9+jA8FERWXswFal3dzzr2wFAlatI65N9xTu3q98DJaVaAF87IbyIiAS6LQJ1yw8BoLxS+iv+tvonify2uxRI2+kzb3tFxOv1Zqzi29aa3Ky9wMWGAQA1oQpan/QgPcdy9kB5KTEHXucPBPwiIuFQtrnwAIBiWF1LzhH0Hn3veFu9iLacz1HbYYYWdv8pXq/Xqy8A9N7pNn1GY33b8TM9ph5U7pIAAAdAZVof89x7aW1NmeluzvyvrTGUCxWmVAb+8MCVoV9HpqMus/Cx5fm5mUfhqDZW2u8PnDw70J+a5F4e7wuFRQLBhbGW5fHrM4mDA0MZxxZ4Of1SmnCoL5xRgPFhIJi4Rwe3oB8YW56zrb750vpF9Gtof/EPTU70N0tsfnRkOmr8V8YV5kdHpqOpf3RcPWtppweCV8a6mlOqZKq96amk3lbGH1x8EalPw+LsfG4LUJ55jqB4j/YMHE0foZ7ag2o7PZzoEyWHq++uRveOeo2+j7e1Z8BqoLs+gX33yd0py/mK9cfPJBYHTsmkuD0eAFDlKtD6pF+1tefM8d27T4xV6JPxPvE7Kk+lDLyINPdfGXKVhY8tj4+OhKZNcVg0Gp4OjYxan7403jcSMh8cng6NuMn3u7tckcuMzY+OFFZ9ERFpfuekX0Sij360WJt9eW46KuI/+Y4pEC7oljNPD4dGsu434ITL7z397PnRvtSzXRYA1IjV++aFejM4XPV998l3z/ayH7L2gMnpAABdZVqf1Kt6jw4MDw8PDw+YtpyTvWcRWitUnFIZeBEthH/kNAsfm78eCkdFxD8UvKLlTvVlx6Ph0OjhjAxzOBw2EsDaoSOhsItZ97H5Uf1yyTJi83PXp8Omy2nrxVlmja0+dFSmfvbIdMqBev2T1e8aW1gYs01km57xOyf909FodHpuuT89270UFnP87rx6jp/Y8vz10HQ4FMr9vLMU6+B7t30asfnrqQ9SkudLNDwzP9Bld1ednZ1ZqrWzs1PATQEVsXp/rv6MaV9ck7UHjjdt231yd05sitH35i2gjiiekZGRohwjItvb2wVXx1rNv0tr/gYBByrT+qzen5PT7/W0Ws94ZziXEw7biKoquXQNVokoloEXcZOF17LF2jBsIxBr7hqbmBzyi0h0ei7z9EBwYsx0aDAgIiK/vnKyT7jpcskymvvHJoIBu8sVsUx9CXvTDSTqH55xucG7noOX8FJalbWLBM4aAWyBt2x5elf/xIL+3POUx/duEvvxkXb2FfNWAM1dY/r/7KKvNgqpHKCa3Sd3p+YeaBvpavb21p49mJu672rOulbMM3M5ekF33RUEADgIKtP67K7etzt+iugdVUK5DLyI4yy8EdKeTc+XNvefDUyHwhJeWh7rMp/tHxpILazlsF/E2T5jsfkZm8tJ18CQPzwdzbhcMcs0brY75QJd3QEJh7WY083cbZsnpMfvxjUKvWXbL8g43UWNnRRr+72nHTWx0G/5h8NvOv0fA1Bjdlfv33WQI1+9P5X1qN3VJ/dXLWesO6iC3eT4Ih0PERGZnJzMeUx/v+UrEgCKrkKtT76tVa56HAhO2pH80PokKBnAOwvhY69+FRHxH27J/JsemP/6KibmRcnePJwe8mkxm5MAeONVVCQjgk4pJv1yOTkv0+5m897gXQ/9zbGuHq8na1PgLWf7gvRR/PnUPI/vPVtpsZhsbPz4aunRo3Du+kQiEcvPtaH1DQ0Njq5phRGVAKpEIa+yQiReg5WqQNnQWABAJofvxoPQWCgawDsJ4fXwMiMoF3EVmJvp67OnM8+gtlxXXuc6Ee68zGw3m5+MCF4fWp4Zrhd4y0Wss4gU53uPLc/PzUyHSbcDAAAAqCIKzoE3NPef1WZKsy98SXR1axPo9XnwWvyeMcmgVJoPv1mW61iILY+PhBLRu9/v9wcCQ8HgpDaFHgAAAAAqRNkMvIhI11gwEA6FRcIz8wNX0v+abbh0tmHWWdhOj07Ktrp7vhyU6X5seG4pOfjM7ePcVC8b6zrr35AD6UcW+r0vz4X05QAnx1IKiL1yWCMAAAAAKAWFM/AiWggvIhKdvj6XHl7pOVzLRcOLP+JcixudLlhf9DJtb3Z5vK+vry+/fdVNOfj07eNcVs9S1v3m9W8oU86rFfi9J1bzH0sP/22rBAAAAADloHgAbwrhwxkzlo34M2MTtcTq6Varr+XLCEetNimLzY/29fX1jbrczc1NmWkD3g16OOp2qIGua2DILyLhpfG07eNcV8+S/fnGN2Sm/1yQHpjre8aZa12S792qSgAAAABQRsoH8MkQ3uIvA/rG3yOj88t6MBdbHtcXoiv2dG5jTn441Je8nPmCFtulFbFMI9gOjY7rB8aW50dDdhu1OUub6yF2OJy6/Hwe1ctxe6Pj87GMbyjtYH1WfDg0bv4yQ5lhtfvv3fQ09N8JTFeRmF2VAAAAAKCMlJ4Db0jMhU/X3H8l+Op6KByNTodGps1/8QeCV1yH07nrMRkUy8uJ+Icm85oo7rxMY13+aDg0Yn4W/kDQfGV9inh0eqRvOvf8ddN2bpZ560JvOXF+eHokbDrf7/dLNG3btuSSB+YbDASDEkr78p1/7xZPI7m9QcpjFP9Q8OSj0HSRFxoAAAAAAKdqIAMvkiUL39w1NjEZHAr4EwuI+/2BoeDkRMYM56LIvJx+vYWJvH8ucF5mc//EwmQw9V6DkxOpUXRz/0RwKHFI7hnlWg7efrxCobecOD+10ldOWhzbNZZ6ZGAoaPMbgdPv3eppNPdPpD9F7Xa69Nn1lpP2AQAAAKDE6vb39ytdByBDbH50ZDoq/qHJ/H/6qB6dnZ0iEolE8i5hZ2dnfn6+eDUCgDwNDw9X5Lo7OzvaPxoaGipSAbfyePPTWACAHYetz0FoLGokAw8AAAAAQG0jgAcAAAAAQAEE8AAAAAAAKIAAHgAAAAAABdTENnKoPc39Ewv9la4EAAAAAFQRMvAAAAAAACiADDwAqKnt9HBPq8jag6n7q8Zn9W2n33u71esVEZG9tWc/RZ6s7uYop/74mYGj3oyP957N3X1inFtf39aZLFj29taiVkWnXN7p9S1qVKK7cFVDo8DUErSHnl4qABwg5Wx90greW/vpu/vZ2568m578C8rZsjhqQ7PfKa0PksjAA4CK2k73tIrI3rNIsv90/MxAT6KDIOJtPdozcOZ4fY6S6v+Q2X9KPeD4mYGUgsVrVXTb6TNpRzm7vsXVSnEXLmvY1mnRrRRZjTzbE/Eefc/tbQFAbShj65NZsLe1Z2D4dFtqffK6uIOLOSsoZ8viqA3Neae0PkgiAw8A6qk//nariMheNPH7fP3x97SQc+3Z3P0nu0Zqw3u0s+1JMkmSqa21VUQyc9XJS+nlGgVLfdvx93qOesV79L3jq8Y5bad7WrXLP5i7v7qbSKx4/W31T5znC0p1F65qWN92eqCn1fpCu6vRvaNeb84KAUAtKmPrkyxYP8J4bbf2nG5b1UtOf7O3nX6vp9Ur3qMDp3en3Lyk87yLnC2LozbUyZ3S+iCBDDwAKKe+za819aYelPHRs4jWEdp98tOaiIi0vp3t9/r6en24XtRmJHn9H2Rvz1yw7K4aRXv9bVrRRpdO1h5oQ/52n/y0trf27MHclKvRfqW6C8c1rG87fvqMbfQuovWhclcIAGpRGVsfqa+Xvb29PZG1n9IKFm+9XnDyR4CI9mZfvW8c09rall6ikxtzdRe5WxZHbaiTO6X1QRIZeABQTZYelOztJnpCu//cE/GKeP9QL2IXRRtDGE3npdhdvX/X/qd+vejk1f+ZKCXreXaVKdVdOKyhKfO+tyem8Y5mehbE7dgCAFBeOVsf2V19cn/1Scop9Wmv5cQHpkJW19Z6WltFi+BXHbZD+d2Fg5bFURvq4E61smh9ICIE8ACgHKuOhtVUwt1drfOh/YJvM65cz16I9+0zwz1ax8x6kSDLs/ROS+Lqe7ty/PSZo61eyWshodLdhasa7q09++7+buewTSLeqBF9KAAHS2Vbn/rE2iT2g61SZLl6Rtl53UW+bV96G5pRrs2d0vpAQwAPAIpJpC0sWn7L3kCW9EHiR36vN/mv1p6BM60P7tpMsqs/fiaRpk7vRLX2DCSv2nq0p9Vfn8+KuSW8ixw1TPa8soy9dJRcAoBaU7HWx7Re/d7as+/uZzQrphjbMnntlMu7MLhp+7K1obnulNYHIsIceABQjdWQwbzLMibnrT2Ym5qampqae7C2JyLibe05bRXCpuz6Y8zWM9NLmnumz/Arx4q57u4iSw1X79+972DMwO7unvYP0+REAKh1FW19DN5Wf2eb8epNzBVPvMnrTVuIeP9Qvle0w7YvZxtqknKnGlofiAgZeABQjZMN05zKmJy3u3r/p1Zt7Hjm9MHUPXvXHlik6Nd+MhbyiTzztx4tz1A/V3dRzBqSBAFwgFSu9dl9cnfqibFFiLe1Z+C06EvMr95/oJ3lPTowfDT1GjYD1EvBUcvioA3NcqcpaH0ONjLwAFBDLBMO7joxq2uZi9+KttWtuedhuT+P6UrJRIH7HEjp7qJYNQQAmJXwvZ2wq22GLikrsa/en3vwbG3PuOLagwfP9izPdiLPu3DQsjhqQxMs7xTQkYEHgBpgTIwzy3O0o9X8wbbTZ/StbkVkby19hqLV1fNRursoVg0BAGYlbn3Sr5ZYWM6Ugk5bxL3++Nuur57fXThuWXK0oZZlW90pICIE8ACgGsseg9VqudmWG9K0nT7ztle8Xq85GWBaVlcv57i55/HMYmUeY28b85jBZNfHeQamdHdRrBqmKuP4TACotDK3PsYxYmp20t/b9W2n33vbK17vXqKcxFL5a2vO9zLN7y6ctSy521And5qK1udgYwg9AKjFehEbY7hdciWf429r69yalslpOz08PDw8PHxGH5G3uranrf/b2nO6TV//57S+PO7es4jW9zGtCGTZ8xDRejFivnxyHaHk5dOvbqF0d+Gohg4VcyEnAFBFmVuf3X9qK9R7j753XD/m+Htp26vtini9Xm9ypHnikMTb3+LqFvK6C0cti4M21MmdigitD3Rk4AFAMdb7yOw++e6Zf+CoN20lH3MfJlNi+R9p7RkY7kn+Ye3Bd3oywejDiFgtEmQkT3af3H3wB4uFhKxX6clyb6W5iyLWUJykZQCgBpW19UmWK96jPQNHk8fsrT1IxMGrkWdva4vGpV38O5e/zOZ1F7lbFkdtqKM7FaH1gY4MPAAoJvmTf+o6P7tP7s49WNtLrN6zt/bsQc5d2FfvT6WcJHt7aw/mpu7rP/knxiLmllaSUY7DsxNKcRfFraGRArHYwRcAalg5Wx+93PRj1p49mEuZQb775K55ETutlJwXt7y7fO4iR8vitA11cqe0PjDU7e/vV7oOQI3r7OwUkUgkkncJOzs78/PzxasRVGfsRWM7pL1qtZ0e7pEcq+9WOXUffnEMDw9X5Lo7OzvaPxoaGipSAbfyePPTWKDqqfsCpPVRnsPW5yA0FmTgAUA5RhbE629TaXuZ+rbTb7e6WleoGulDGMmAADh4aH0qiNYHOgJ4AFDP7pOf1kTU6kPVH3+vx7v3YE7l/EdyPqPrle8AoAbQ+lQKrQ8SCOABQEX6irkK9aF2n9yduntf8cSBPp8xx7JGAFCzaH0qgtYHSaxCDwBK2n1yd+pJpStx4PDUARx0vAcrgaeOJDLwAAAAAAAogAAeAAAAAAAFEMADAAAAAKAAAngAAAAAABRAAA8AAAAAgAII4AEAAAAAUAABPAAAAAAACiCABwAAAABAAQTwAAAAAAAogAAeAAAAAAAFvFbpCgBwpL+/X0QaGhoqXZFi2tnZqbE7EpGdnR3tHzV2a3xZCqnJLwsO0ViogvePQviyUFXIwAMAAAAAoAACeAAAAAAAFEAADwAAAACAAgjgAQAAAABQAAE8AAAAAAAKIIAHAAAAAEABBPAAAAAAACiAAB4AAAAAAAUQwAMAAAAAoAACeAAAAAAAFPBapSsAwJG//vWvla4CAAXMzMxUugqoJBoL4ADizX+gkIEHAAAAAEABBPAAAAAAACiAAB4AAAAAAAUQwAMAAAAAoAACeAAAAAAAFEAADwAAAACAAgjgAQAAAABQAAE8AAAAAAAKIIAHAAAAAEABBPAAAAAAACiAAB4AAAAAAAUQwAMAAAAAoAACeAAAAAAAFEAADwAAAACAAgjgAQAAAABQAAE8AAAAAAAKIIAHAAAAAEABBPAAAAAAACiAAB4AAAAAAAUQwAMAAAAAoAACeAAAAAAAFEAADwAAAACAAgjgAQAAAABQAAE8AAAAAAAKIIAHAAAAAEABBPAAAAAAACiAAB4AAAAAAAW8VukKAAAK5Dtx+eMPOpuatP/a3IxE/vH32cfrBZV54vLMpU69wIVPP5nNLM13YvDDDzqdX9bt8QAAe+V88/sGv/6qr8lBAZGbZ288dl8+ABcI4AFAZZndqqamzr5LnZ1dN//7Rt5duROXjT6W06tmv6zb4wEA9irz5i9UqcsHDgiG0AOAuk5ctkuKNHVe+njQl1+pvsEPsnWysqVimjovfXX5RGHHAwCyqMibv2ClLh84MAjgAUBVObIZTX15deR8gx9nGymZ488i0nnJHJK7PR4AkEVF3vzObC783W78fFHKByDCEHoAUNaJLlMnzphO6Dtx+eNLnXo3qanzHd/suqvRlLk6Wb53Ok1/jtz8VBut6Tsx+PGlxImdHwz6HmuTG90eDwDIoiJv/vXZT87OWlcnOas9ctN2UjvhO1BEZOABQE3mXtzmwn/r/ab1xzf+EUl83tT5jqtMTO5OVktT8u+Rm4m5luuPZz+5aXVdt8cDAOxV5s1vW5vkaIDITdvV6wjfgaIigAcAJfkOJ/tDm5EfTWmPx8umflxTi4syT3yod7I2I5FN1zXa2Eye4+S6bo8HgIOuqt78zsL3AlsWAOkI4AFASebU9uarlGGLKZHxYceJmGRfLPKPvxfay3Jx3byOB4ADqIre/KbwPcvc9+K2LACEAB4Aas/6qzxS26ZOVpZMSkoXsfPS14Mn9F6i78TllDGSxnXdHg8AyEcp3/yZTGvKJ0fyF7F8ALZYxA4AVJQyjnJzo9Di0jtZtsmb9R8jm32JyLup79JXfZeyFez2eACAnUq9+TPP/DDxXo/8w3H4zkAroBjIwAPAgZdMpWQbCKlZnzUtleSA2+MBAOXg5s1vd6ZIZNl+7bp8yweQDQE8ABxwyQWCsw2ETHp849OblisRbW5azm90ezwAoNTcvvlNTOl3+9C8gPIBZMUQ+sItj/eFwrkP8w9NTvQ3J/87tjw/NzMdjqb81fLD4ostL0tXV7Jk7RZKdrkql/40ACWsv9oUMTb9bWoRybd7lE8na/3xjU82Tgx++EGfvu/w5mbkH/99Y+Odr7/q0w9JGdzp9ngAgIWKvvkNpp3sUhfCL1L5ALIjgK+Q5fGRlKj/zcPNNh8WWWx5/HooHA0EFwhZeRqoVS5mSZq2580yj9HK+uPZG49nUz468WGWnX7dHg8AcKEsb35xFL8XVD6AHAjgiyYQXBjrcnrw8lJYP2lyLBE6Wn5YbBtLRn7fpGtsYWGsRBesapZPA1DDxqYpEXPYZ07EZNlnKI3vnc7EoZ2XZmYs15dr6vtqpk9EIjfPZl1E2Nx7zHHdvI4HgIOu8m9+U/xud5mitiwA0hHAV1Sg2yJQt/wQAFKljKTsfMc3u250pIq8THEa34nLH3/QJCJNTU2bC59+kkiumLtspmWN3B4PALBTqTd/gvkyvLiBimAROwBQ0+Pl5OruTX0f6zus+04MmvZXt52fWIDDTRoRaer7UN/X3Xfi8ld91t06t8cDAOxU6s2vM//yytIlQGWQgS+3lCXvwqG+zOXvjA9NY/Jjy/NzM4/CUW24t98fOHl2oN8qTx+LLc9dn0kcGBgyH2e+tH4R/Ropi9jF5kdHpqN2S9ppf039o+PqWUs7PRC8MtbVnLGunl77zJkKdn/IUSv7p2Fxdj63BZTc4+XIpU5jLKPlDuupvbgTl2eMLXmNQYvrs5+cnRULvsGvEwF2St48bWP3zktfWYyONMfj6hk0rwAAIABJREFUbo8HANirzJvf4GicfgHlA8iNDHz1Wx4fHQlNm0LJaDQ8HRoZHV9OOzA2PzoyEjIfGJ4OjWQclkvzOyf9IhJ99GPMojJz01ER/8l3TIGww+pZyzw9HBrpc13pNLGCahWbH+1LPdtlAUCZPL5xM9sW6yVa+zfXxu6bC5+mTGh0ezwAwF5l3vyaMo3TB5ANGfhy09aLs8waW30Ymx8NhaOSSEyLSCw2P3d9OhwNh0YPm/Lgy+Mj0ykHxrRF7cOh8W6twK6xhYUx20R2UvM7J/3T0Wh0em65Pz3bvRQWc/zuvHqWrE5fnr8emg6HQo6fqFWx1/Vih4JX+vVix6+HwlFTrWyfRmz+euqDlOT5Eg3PzA902d1VZ2en9R9ERGRnZ6eAmwIsPb7x6eGvzWPRkyI3S9aJy3JVy5yK2+ORxf/8z/8UeICIbG9vF6k6FVDz79Kav0EUrDJvfpSak7d3Kc5VukXIoobfpWTgiyYc6stidN4inZ2blvAW/9DkRCKWbG7uH5sIBkQkOj2XyAfrS9gHgokDm7vGggERkfCMy4vrOXgJL6Wlm7WLBM4aAazz6jm/u67+iQWt3vkyFdufLHZsYnLI76BWsR8faWdfMW8F0Nw1dmXILyISfcUPzqgq67OfnP30ZmRzM/HJ5mZk4eanZ288LmEnbn32k08tr2oTjbs9HgBgrzJvfjcr3QMoGTLw1Sw2P6NF5WczUr5dA0P+8HQ0vLQ81tUlyfi9OyVj3tUdkHBYizndzN1u7j8bmA6FJVG8Ro/fjWu4qJ4lo862p7uosZNibe5K0o+aWOi3/MPhN0XYdA5Vaf3xjU8cDEJ/fOOs46HqtlMYk0c4vGq+x8Pav/7rv2Y/4L/+67/KUxMAFVWJN7+bwvIp/2DL+XrPgjf/gUIAXzSu9oF3ZuNVVCQjKtfo4eSvr2LS1SyxV7+KiPgPt6QelfcG73rob4519Xg9WRvn1bNkV2eRxCj+fGqerdiWw/5ctUovLRaTjY0fXy09ehTOXZ9IxHpWmja0vqGhwdE1rdTwKCAAxdXQ0LCzs1PIC6c6JV6DtXdraWgsALiV33uDxkJRBPAKsFyrXqcn1/VY+s3DRVsjPSOC14eWZ4brDqqXTRHrLCKS9VHoPyvkrFVseX5uZjpMuh0AAABAFWEOPGx0dWsT6PV58Fr87h8aKPIoAxvNh98sy3UsxJbHR0KJ6N3v9/sDgaFgcFKbQg8AAAAAFUIGXgEOBue7HxueW0oOPnP7ODfVy8a6zvpAeAfSj8z2KLINrzcsz4X05QAnx1IKiL1yWCMAAAAAKAUy8NWs5bBfRAtxc9AT1pkrpC+P9/X19eW3r7opB5++fZzL6lnKut+8PhA+U86r2T4KyT68XpdYzX8sPfy3rRIAAAAAlAMBfDUzQlyrjc9i86Pm7enSBrwb9HA0a8rZXtfAkF9EwkvjadvHua6eJfvzjfXtzfSfC9IDc33POHOtu202z0usmm+16l4uVlUCAAAAgDIigK9qzf1ntWA01Dc6v5yIR2PL46Mj01Exx9RGsB0aHdcPjC3Pj4bsNmpzljbXQ+xwOHX5+Tyql+P2RsfnY7H0s9MO1mfFh0PGDUpseVy/wxT6o4hOjyRrlSzVahq/6WnovxOYriIxuyoBAAAAQBkxB75osi3GrslnsnjX2GRQrofC0eh0aGQ69W/+oUlTec39V4YejUxHo+HQiLki/kDQfFV9inh0eqRvOneVTNu5WeatnVcvx+2Fp0fCpvP9fr9E07Zt6xoLBsKhsEjKDQaCQQmlRfHN/VeCryxr5Q8Er5h/U7B4GvpjlLTHKP6h4MlHoekiLzQAAAAAAE6Rga96zV1jE5PBoYA/uQa63x8YCk4uTKSlt5v7JxYmg6YD/f5AcHIiNYpu7p8IDiUOyT2jXMvB2y8/77x6OW4vtdJXTloc2zWWemRgKGjzG0FmrbRKTaTNbLd6Gs39E+lPUbudLn12veWkfQAAAAAosbr9/f1K1wHIEJsfHZmOin9o0tnvANWts7NTRCKRSN4l7Ozs/PWvfy1ejQDUrJmZmZ2dnYaGhkpXpMh2dna0f6hya3m8+WksAORnZmYmj7NoLKpBHm9+MvAAAAAAACiAAB4AAAAAAAUQwAMAAAAAoAACeAAAAAAAFMA2cqhKzf0TC/2VrgQAAAAAVBEy8AAAAAAAKIAMPACUxonLM5c6RSI3z954bHzmO3H54w86m5pERGQzsvCPv88+XndfpmwufPrJrPnElIJlczMSySja5zvxYa5jnMnnLnyDX3/V15TxccaNpBxs8dcc5WgPyLpUACitsr72sx5j86o0pNTQidK99nOXnKPx4rWPA6c0GfjV1Xu61dXER6tZTwGAmnList6d+nuyGzf49VeXEn0QkabOvktffT3oc1tmhvSCpSmjaN/g11/lOsaZPO+ipcm+K5nuxIf2/c4c5Tz++8KmSFPfx67vCwAKU87XvttjClK6137uknM3Xrz2ceAUOQO/eu/iv/0/t5eWEh9c+N/9W20iIiv/0d5+u/vC//6/t95vK+41AaDq+AY/6BQR2Yz8uJ746GMtLI0sfHpjdt3ITTT1fXhi1lEmxD581wtOK9lUdMYh4jsx+PGlviZp6vt48EcXaYt87+JEl1b3nEkS34nLX2Xpi+YsZ/3HyGZfX5PzxwoAxVDO137uY9ZnPzk7m15BIyFu/okht9K99nOX7Kjx4rWPg6aYGfh7F0+0/4s5es+wdPtf2k9MkIoHUON873RqvSRTR874aOHvWk9mffYfERER6fzASdrAvh9nJDkyS5bOrhMiIr6WJtncNB8i64+Ng5o633Getcj3LnyHjeGRP9pH774Tg5e/zha9Oypn/cfIZu4KAUBRlfW17+4YrTLJUPimq7HmpXvt5y7ZYePFax8HTNEC+NWJE/9y24jduy/853/+Z3fK39s79P9e+vd/I4QHUNOydORk81WiK7OxuSkiIk1NLblKzHOE5ObmhojI+uMbn3zyydmzZy17bQ4ub8j7LhI/Mryy7TaeuPzVpT79uW0WUE6iK+fqlwkAKERlXvt5he8uJ7+X7LXvoGSnjRevfRwsRQrgVyf+7d/16P3C/67sP741OvrWn1KOaBt9vKLH9Ev//h/3inNZAKhCVr0Sq7mA66/0QLXpcPYuh9FH21xYiGT+9fGyno3o+1DPWegDObPnu43RjUaU70S+d5G4VtMHX89ovv768gmr4zcjC59+8g+L23RejlEjunIAyqTMr33nx+hHGguLuBs8L1LK137BJZsaL177OFCKE8Df+49k+G4/x71t9P++oP3r9v9HBA+gZiXyDhaRsWW0nD0Xk+yj/ffsK6sDHt/49GZkU0Q6+76amZmZ0RZwj2Sbbe4b/NrI22SN8m24vAtjJKVIU1PyX52Xvvr68glzqZGFm59+csN+bKfDctzkuACgCMr92nd4jCb5o+7Cf+e9UHtpXvuuS7ZrvHjt4yApSgC/+vJn7R/d//l/3s92YGIc/c8vGUUPoEYl+i3Zxnk7Zu6j2Rd3OOOTpqamFptERMrWPpF/lH7jncQsxsjNT8+ePXv2rP6DgzR1Xkr05R7f+ORGjm2JHJUjLnJcAFAM5X/tO2satMolRgfk8XNtvpy+rl3J0njx2sdBUpRV6Fde6Pn3P72VfYX5trf+JLIkIksvVkRYjR5ALXKzYVouTvpoyS5N5OanNx6v6wu5N3Ve+uqyZGz1m7oxr9vZkPl5fOOT1KusP77xjy5t2+LOrhPy2GEdXJfT1NQiwsbAAEqs3K99F+F7cvh8OX6uTSjWaz/JYePFax8HQGn2gQcAWLIc3mc3C91RHy2RXIncvKElsNe1XXElc0neE5dTO0AZ4b1Dru7CmjFzv9B0SbHKAYDSKPpr3034/v+zdz+xTZz548c/kfZUXzZRyCFAU+drcwicaGhU51uJrCgiWSli9Y1SfuyW7ikhQruBCqjaVHtY1a36R0B2hSA5bdPdfCHKalGkJohFayS+cRVIOUEO2Bs3JeGQWMle3Kt/hxnbY3tsz4zHnoz9fp2MPfM8z9jt8zyfPP8yK8aXl8r6c23lqn0jKdvVeAE1wZYReH9HQBlYf/o8KsWOec/Mte/w25ExALjE+suXIjlDNKUmXWb2AOr/bLo/6yPlnZdzH168pbf0cu2Fmpt2LKL7/a/OdaaK8HL5+kXzHSArT1FAZoFkeexKBwDsValq/x+tRpqGtZwMrcbvlav2DadsQ+MF1BRbRuB9qR3nwzPfFlvbHv12xuBcewBwrXW9U9D01ucV2/XIPO0gRqYPlEq67ZS2AzT3obUOkLWn6H7/q6+++mp6elq78DE949R4B9B8OvZ8swBQnEPVfmmZ9e9WM6xctW8sZbONF9U+6oAtI/DSe3JIJidFOSFudEJ/I7uFYX96r/qTRfe6AwAXywx/72/LLMVb/Pvcrzr7W6W1/3envrt4ay2zL7BmXWL3+8oKQWX8ZPHK6dyOSvYFSnZLy+c6O0Wk89z73evKGvj0iUGpLYvS72TdWjrxXBaeQmRx6eW5czklTB1ebOZMI8Pp2LufFACUUOVq38g1ilJ/KnW02jeSsqHGS6j2UV/sCeCl99K1wOT5sIhM9jU8vTb/9WhWhB5dGH+vLxW9l9yrHgBcLTUvMHsvnbVbf57r/Ky/NWfWo/lTeXMtXrnepYxQdJ77bPpc5oP0+sh0p0hEb9KlmeWElp5i8cr11N5F2SVcvm7qTCOj6VR7jAtAvatytW9U/mwsCypX7ZdM2XjjRbWPemLXJna+0cV59ZB3CZ/v8zc09E2KiEz2NTQ0NPgz0bsErn09yvx5ADVs7btl9UDa7H3V1m5d/PD6cmampXLuuQ3bAi9eufjh9TltyllJp+dQ2sLSUyxeOZ11k7x8uXz9w9NXih8bZzGdVJe1mkcmAahrVa/2TSpvXLpy1X7xlI03XlT7qCsNyWTSvtSi493+TKieLzA0//VEsV3ugFrU2dkpIsvLy5ZTiMfjv//97+0rESotddxN0Rl/u1L3+9PnxN0b/Lr3y7fH9PR0PB5vbm52uiA2i8fjygu3PJqFmp/Gws3cW/NQ7deC6elpC3fRWOwGFmp+e4+R840uJpOR+WtDgUBA+34gMHRtPhJJLhK9A6gHqcGY1s433XS2WVv3+7/qLPesIcepMykZiAFQRVT7DqLaR32pwDnwvt7RicXFxaTG4uLEaK+P2B1AvVi79Y9lEXd15dpO/e5c68vrH7p5GCazZFKzuxIAVB7VvlOo9lFvKhDAAwBk8e9zL8VVXbm1WxdPXzS9Kn2XUZdMVm+PKABQUe07gmofdcemXehzRaPRSCSi/sPv9/sYfgdQZ9ZuXTx9y+lC1B2+dQCOoQJyAt866o7NAXx0YfzLT85P6u1jFwgMfcwOdgAAAAAAWGLjFProeHeDv08/eheRcHiyz9/QPR61L0cAAAAAAOqFbSPwC8PaA+QCgaFDgx0nDxwQef78zsrMZCqsD5/3d0tkkZPgAQAAAAAww6YAfmG4b1J9mXvWe29vr4xOTEQXht/rmwyLSPj8e+O/JIQHAAAAAMAEe6bQL9xJhe9D8wXOevf1TizODymvw+e/XLAlXwAAAAAA6oQtAXw6fg9cu9Rb5LreS9cCyqunz1kKDwAAAACAcfaeA3/oQPGJ8b4Dh5QX4ZVI0QsBAAAAAICWLQG8vyNgRzIAAAAAAKAAWwJ43y8HlQh+8k7xte3pufZDJ4tNtQcAAAAAANnsmULvG/14KCAiMtk3vFBwdXt0/BMlfh+anyB+BwAAAADABLvOge+dWIx0DL93fnKyz/805yA5EZHMKXKBaxHCd8C0P/3pTyLS3NzsdEHsFI/Ha+yJRCQejysvauzR+LEAV6CxcItarX/4sYBKsyWAXxju/uRp5p/hyT7/pIgEAsrE+nA4rL165r3uGZ1EDn28SGAPAAAAAIA+m0bgc2L0Im8WeFfkkD0lAQAAAACgFtl7jBwAAAAAAKgIW0bgeyeSyQk7EgIAAAAAALoYgQcAAAAAwAVsCeCjC4XPjgMAAAAAAOWzJYCP3OnzNzQ0dHcPL0SJ5AEAAAAAsJ+dU+jD4ck+v7+hoaF7eJxIHgAAAAAAG9kSwPdemr82FMj8Ozx5vs/vb2jo7h4eZ3I9AAAAAADls2cE3tc7OrGYTCYj89eGAplIPhyePN/HkDwAAAAAAGWzdxd6X+/oxKJeJM+QPAAAAAAA5ajQMXKpSD4SyZpcz5A8AAAAAACWVPgceF96cn1OJK8MyTMgDwAAAACAIRUO4EVEJLowPv7le33nJ8O5nygD8t3jBPEAAAAAABT1s8olHV0Y//KTmclwdtgeGLr28aVf+iPffvlJKqIPn/d3S2Rx1Fe5sgDO6+zsdLoIAIDdjsYCAFCE/SPw0YXx8eHuhoYGf995TfQeGLo2H0kmk4sTo72+1Mz6yDV1Vn34/JcLtpcEAAAAAICa0ZBMJm1JKBpd0AyqZwSGrn18abS30Oh6dLzbfz4sIjI0n5zotaUoAAAAAADUHFum0C8MN/RN5rxXInBP8R04JJK3Nh4AAAAAAGSxfQ28wcA9/74Ov91FAQAAAACgZtgWwFsN3HsvRSITPvavAwAAAACgGNvWwAMAAAAAgMqxZQQ+Gl2IRERE/P7eEoPp0YWFyPM7d+SkldF6AAAAAADqlS0j8OlN7EruJG/8SgAAAAAAkGH/OfAAAAAAAMB2VqbQR8eHv1zRvvH0aerFJ8PDd4rc+XQy97Q5AAAAAABggKUp9NHxbv/5sg5vZwY9AAAAAABmWJpC7xv9eKiMPAND80TvAAAAAACYYXkTu+jCQiT1+vknfcqAfODa/McHit7m9/t9nPoOAAAAAIBJlo+R8/VqjoG7IxIWETl0oLeXsXUAAAAAAGxnyzFyJs6BBwAAAAAAFtgSwAMAAAAAgMqyPIUegFGdnZ1OFwEA4IDl5WXjF9NYAEB9MtVYWNqFHgAAAAAAVBcj8ECVTE9PO10EAECVnD592tqNNBYAUD8sNBaMwAMAAAAA4AIE8AAAAAAAuAABPAAAAAAALkAADwAAAACACxDAAwAAAADgAgTwAAAAAAC4gDMBfDQajTqSMQAAAAAA7mTvOfDRhYVv79xZKfTx06dPRcLhsIgMzScnfLbmDQAAAABADbMvgF8Y7u6bDNuWHAAAAAAAyLBpCn103FT0Hhjq8NuTMQAAAAAAdcGeAD767Uw6eg8MXZufvzaU/tf8/Py1oaGAZD6fTy5OjDJ/HgAAAAAA42wJ4DPxe+BaZHFitLd39KQawYdFentHJyYWk5FrShAfnvxknB3sAAAAAAAwxZYAPrKSit8Hf6mOrPs71DH3p8/VaN03+nUqhD//5YId2QIAAAAAUDfsPUbu0IHUzHjfgUPKi/BKJPWpb/RjdVx+8g4RPAAAAAAAJth7jJyGvyMgEhZlCL5Xjet7Tw7J5GTOmwBQy8KfH/3obkND8YuSSf/Zv06e2ledImVbvzX0m5vP5YBjBdhV1sNhCQT4HgBUitoonAiGPgiUvtqE3Oor/PnRjxYqXbfTgmjRgqAqbBmBT8+X10gPwevSjMsDALALrIc/H+p5d+zhj04XBADMofpyHD8BqseWEXjfgUP5o+2pIfjwSkSEwXYAdc7+wRbY7MeHC8+l1FwJANh9dKuvwAcPHnzgTHnqEC0IqseeNfCpIXjt9nTpIXjNgveFO5PKiwDnwAMAAAAAYII9Abxme7q+hm71lLjek+n3hhei0Wh0fLhPjd81u90BAAAAAIDS7NrErndifmhSic/Tc+Z7L10LTJ4Pi8hkn39Sc3Hg2qVem/IFgJoT/rxn7G4yeeLTBzmT7vO3PlL2KOr99MGv5fM/jt2NiEjSf+LsHz5IbSa0Hr71t7/8ayESaRCRpN/f+4vf/vpUwR121sO3/vaXm3fTF//2D78O7NO5WE31eSQ1XdDvP5GXsB1lU7dH6v30wQeB9fDnf/xIW7YPlGvXw5//8S93IxERSSb9vZoM8ktcKK/w50fH7jYosx/vjvXcFUmeCGq+fiNfY8nnBQDzStW3hauvvE3sNI3Lq+HP//iXdJ124qx+y0ALYigvWhBUmX270PdOROY7vvzk/GQ4/ZZvdHF+pbtP846IBIbmvx5l/B0A7PPw89/cvZtaevdcXlXa+0x/Rf2oIRK5Gxlb+NeJTyd11uOv/W1o4m4k6+Kxdxf8uRev3xp6dyIiItrFfpHI3cjY3X+dCOYnbEvZlH5nTtn+PfzNH17927tjd9NXNTRE7k68+2/5Jns35PDnQ2byys/bzO36zwsA5pmtbw16+PnRu3e1ddrdibG7azkbtdCCpG+mBcEuY+c58L7e0YnFZDI5oRle751YTEbmrw0prs1HIosTnB8HAPZpaLh7926Dfzj4TSgUCn3z6V9/HRARWb/1x4/uRhqSydQnodA3wRP+ZLIhcvejoVvruYlE7t6NJP0nUtcWuHj91h9vPhcRzYXqpSLyfOEvOenaVLa7Y2N3/SeGv/lGvXTYnxSRyMS7747dzaSRev/5zb9p/m68fmtIzStd5G++GT7hz8or8MGDUCh4IpkUkRPBUCgUSg2eGLq91PMCgHnG6tvC1Zc+tZo6oal8k0kRWfjo86yakxZEyYsWBLuPnQF8Ib7e0QnFaK+P4B1AXbo71lNEXjtuTvJEcFKdj7dPnbEY/ttEpEE9Xj41U29f4IPJv571J5MNkYm/hfMSSZ74dPKD9Ky+fYEPJj/tFRHtxev/969IQ0PSP/yHDzTT//YFPvjDWX8y2dAQWcs7Qceesp0ITn5wSp2LuS9w6rfqH4qT/uFMGvsCp9Ri3H2YTkGTV7rI+/ad+mDy0xMF8spi/nad5wUA8yzUtwYlTwQ1NVrgg0+VGvXfP6YbIlqQvO+BFgS7hy0BfDS6oIhGS166sLAwPjw8vlDySgCAQQfaXs15J/xwQUSk97e5y+f2qV2XhYe5HYfeT/NGbQK/VkYk0hfvOzUZCoUeTOYtytv36n9VtGxvZRft1TZ/MqmTRm4x1m/9pUBeEvj12QO6eZV5e/7zAoAFFupbI5JJ/9lf69eoabQgIkILgl3LljXwkS/7lP3rhuaTE0WH2BdSV8rJUWbSA6gjFT0H/r9y18mt//hvkQJdgVfbDohE5N8/rktmDCSZ9Otdu++/f3FgIpJ7cTqX9XX58cf/+/Hhv7TbEVWjbPte/S+RiG5+WX5cey7SkNd706ah/2jWb897XgAon6H61pi8WkqpzpQB8H1CC5JCC4Jdyr5N7AAAu4Xab9DtCqj9hueprprKcLdhPXzrb3+ZuJvd+THRm7RQNn0Fuow6lG2B9RnIq8zbAcCicutba2hBstCCYLexEsBHx4e/XNG+8fRp6sUnw8N3itz5dHKyyKcAgF1uPfz5bz66mzn7xy//9V+/eOut/371xz/+5mZ5g0IAAI3aq29r74kAJ1gJ4H2/7Hh6/rzemo9weDJcfDcH1dBJDoIHAHPUmYNG6E4kzE4mdwJisYmAaeG/fXS3oSGZPPHpXz/Iunjd+GZKFspWrjLXL1R0+QMA6LKjvrWGFiQLLQh2G0ub2PlGPx4qI8/A0PwE8TsAFKfZENg0dSue53qbFP+49lwkd8JjgQ2N1//vX89F5MAv/lvZ/FfdPOjTD3I7Tj+uGR48MV22MrzadkDE+ldZ5u0AYJkt9a01tCAKWhDsUhZ3oe+diMxnXEv9XSlwbb6ESCSSXCR8B4DCXm3TPVAn/DcTMwwDbym78eYeq5vZFjdvW538a9NH4Ki9r4JSiVasbFbt++9fHJDcc33TuQ319PQcLXaCX5m3A4DtzNW31tCCiAgtCHYty8fI+XozDhxS3zx0oLcEHwfBA0BxqZNsFj76PKy27evhz4fG7poZcgn8etifbGiI3PzN0C1tKr+5qRzD++u8Hk5DZOI3Q5ocb30+9FHOETjqeIKmYLK+Hv586DcTkcqWzbJ9p357IplsaLj70dFMXkpu705ERO98H+1oiYXbAcAOVupb2wZ7aUFEhBYEu5Utu9D7L83PnxQR8fvtSA4Aak+xfWhFRCR5IvhAXScX+ODT3rtjdxsa7o69m7npRDAoH2W2/yll36k/fLr2x4/uRiITY+9OpN9uaEj6T3z6h9xOQzJ54uzZf09MZOUoDQ3JE8G/Zlbv7Tv1h7P/+s3NSHbBRBr8w8Ff/OujiUiDoXWQJstWnsAHf/1U8vMSERH/8DeapYnq0srIxLs9E+mfw/jtAGBCoUYhmfSf/evkqX2m6lvd6qsctCAKWhDsRpZH4LV8vvT4uh3JAUC9C3zwTXD4hD+p/CvpPzEcNN/S7wt8MPnX4PAJfyYd/4nh4F8n89YfiojIq6cmszP1nwh+8yDn2n2nJv/66YnUX2uTSorfhCZPBdR1if/6P0MDQCbLVpb8vDLF1ma279Tkp8PpP0Snh1GM3g4AtjJR3xaovsrKnRakQF60IHBaQzKZtD/VaHQhoj3g0e/3M3ce9auzs1NEpqennS4IAKBKTp8+LSLLy8vGb6GxAIB6Y6GxsGUKfUZ0Yfi9TwodJRcYmv96opc4HgAAAAAA02yZQq9aGO729xU5CD482efvHl6I2pglAAAAAAD1wbYR+Oh4d99kOnYPBIYGPz55QEREnj+/MzOTiuvDk31+mU9ykBwAAAAAAGbYNAIfHX/vvBq9B4bmI8nFxYnR1Mlxo6MTi4vJyPxQav+lyU/GGYUHAAAAAMAMewL4hS9T4fvQ/KL+Mndf78Ti/JDyOnz+ywVb8gUAAAAAoE7YEsBHnz9VXgSuXSo2N7730jV1FP7pc8bgAQAAAAAwzpYAPrKijr8fOlB8j3nfgUPKi/BKpOiFAAAAAABAy85d6AEAAAAAQIXYEsD7OwzOjM/Mte/w25ExAAAAAAA6DiU8AAAgAElEQVR1wpYAPjMzvvjudJm97krNtQcAAAAAAFr2TKHvPanuLy+TfcML+qPw0YXhvkn19dBJzoEHAAAAAMAEm9bA907MZ0J4f0P38PjCQjRlYWF8uLvBnwnf5yeI3wEAAAAAMKMhmUzalFR0vNufmiJfWOBaZHGU+fOoK52dnU4XAQDggOXlZeMX01gAQH0y1VjYuAu9b3QxMj8UKHxBIDA0T/QOAAAAAIAFP7M1NV/vxGJyQqILC9/eubOSeb/j5Mlf9vYSuQMAAAAAYJG9AbzK19s72ssydyCLqbkxOeLxuPKiubnZpuLsCvF4vMaeSPixXIUfy0Vc92NZng9PY5GP/6RdhB/LRfixdgMLjYWNU+gBAAAAAEClEMADAAAAAOACtkyhj44Pf7lS+jKtjksT7GYHAAAAAIBRtgTwkZXJycnSl2kNnZwQAngAAAAAAAxiCj0AAAAAAC5gywh870Qkcqnwx5HIt8/vzJyfDIsEhua/vuQXER/D7wAAAAAAGGfTMXK+YgG5zzfa2zs6emm8239+ss8v85EJzpgDAAAAAMCMKk6h941+fS0gIpN9741Hq5ctAAAAAAA1oKpr4H2/HAyIiITPf7lQzXwBAAAAAHC76m5i5ztwSHkxeYcIHgAAAAAA46obwEefP61qfgAAAAAA1IiqBvDRb2fCyqtAh7+aGQMAAAAA4HI27UJfQjQalci37/WdV+N3OXSAY+QAAAAAADDOlgB+Ybihb9LE9YFrlzhHDjDn6VMWoACw39GjR50uAuxEYwHAAtoCF6nuGngRkcDQ/NejjL8DAAAAAGCGTVPoA4FA6YsOHRo8eemXvT6idwAAAAAATLIlgO+dWGRKPAAAAAAAFVT9KfQAAAAAAMA0AngAAAAAAFzA5gA+Go1GowsLC9FoNBqNFrxqfHh4ofDHAAAAAAAghy0BfDS6MNzd3dDQ0OD3+/3+vr4+v9/v9/sbGhoauofHF3JC9YUvz09O9vn9wwt2ZA4AAAAAQB0oN4CPLgx3N/j9fZPhsP4F4cnzfX5tGL8wrJwZz2HwAAAAAAAYVk4AH10Y7vb3TeZG7gFF1nvhyfN9/u7xaDQVvsvQxxwGDwAAAACAUdaPkVsY9quxuIhIIDD08deX8s54j0YXvv3yk/NKkB8+7/enLmf4HQAAAAAAE6yOwKdH0kUkcC2SXFycyIveRcTn6x2dWExGrmUPyDP8DgAAAACAKdYC+Oj4J6nwPXAtslgyGveNLs4PZf45dJLhdwAAAAAAzLAUwC98eV5d+D40Xzp6F8mK+EVk8g77zwMAAAAAYIaVAH7hTioYNziUHh1/73zWVndE8AAAAAAAmGIhgI8+f6q+Mhq/fzsTFhEZmk/Po3/6PFrkBgAAAAAAkM1CAB9ZSc2fNxW/B65d6u09qUbw4ZWI+YwBAAAAAKhb1o+RM8w3upj8ZXQhIj4RwnYAAAAAAKyoQgAvIuLz9XJuHAAAAAAAllmYQu/vUA91N7+QPb18PtDhN58xAAAAAAB1y0IA7ztwSHkRnvnWXASf2s1O5NABBuQBAAAAADDOyjFyma3ozEXwmfjd4PZ3AAAAAABAYSWA10Tw598bNxrCaw6DJ34HAAAAAMAcSwG89F66pq6DD5/3Dy+UjuGj493+VPgeuHaJ+B0AAAAAAFOsBfDiG/06FcLLZJ+/e3i8YBQfXRgfzkTvErj29Sjr3wEAAAAAMMfyMXK+0cX5le6+ybCISHjyfN/keZFAYOjQocw1T59OhsPamwLXIouE7wAAAAAAmFbOOfC9E4uRk8PvqUG8iEg4nBOxawSG5r+e4DR4AAAAAACssDiFPsXXO7GYjMwPBQJFLgoEhuYjyUWidwAAAAAArCpnBD7F1zux2DshEo0uRL69c2cl9X7HyZO/9Pf6CNsBYFfxtBzsaNvj8Sj/SiS24ms/xDYTptJoOXi0Y0/hj7dWHjzbzMvW+1pbczpfSSQSP62t/LCZKJlzJjO9dAEAFjjUFpjK10r6QI2zI4BP8fl6faO97DAPALuXx3vkSJsn6x3PHk/HnuY9KyvPjHfcPJ5XysxWxOPxeDqO7GnbKpFzy8Fi3TcAgGkOtQVm8zWdPlAHypxCDwBwkZaDeWG0yrOno8Or/5Hu5a8Yv1Y3es/K+cjBliL3Er4DgK2caQss5GsyfaAuEMADQL0oMZTtaTPcbTM1KNLyWuHoXbWnTS9nj/dgscgfAGCBQ22BhXwZgAd02DmFHgCwi7Xs0XSdEmuPH8cSylrEjj1qn8nT3OKJxQzMndQMipRagejxtmVlu7KiLHXMyjg3Z0+L97W2tj3E7gBgN2faAkv5mkofqBeMwANAfdD2nRJrK2ofKbH5bG0r/b6nucVI0KwZFEn8VKKPp50AmVhbSW9UlNh8trKW0L2s5eCRDqJ3AKgEh9oCK/maSh+oGwTwAFAXtDMRE3HtVkGbW5rek6H1hpqrfiqxh7xHfkokEolEIi9bkUTip5JZJdYer2yVvAoAYIhDbYGlfM2kD9QPptADQF3QdotyekKJnxIi6qeveDwixvthCfEcPHJEPQ5I7zCgxGbs8WasZDq6oyvqfPuWg8WLAwAwyqG2wEq+ptIH6gcBPADUu0Tip3TnyfOKgU5bph/m2aPZZM7kIUSelub0vTmD84mtNXpnAFBVDrUFBfO1K32gxjCFHgDqQanRbotJ6X1c9Fi4zGXejkx/bGtNu23R5rPHz4jeAcB+TrUFFvK1p60Bag8BPADAFANrI/d0FO9WZZ8Mv7XC3sIA4DI2tAWOpg+4FVPoAQBmZA2KJLZScxg9Ld4OzZj6nj0tsqkflmdH74m1x4TvAOA2ZbcFDqcPuBYj8BCRjdnR/v7+/v7+q0slLxqd3ahewapiY2mp1h4JyKfd8d3Y9sIFbcbXtrYSCRHZWnn8LHMsXCxrv/hXPLqZtBzMid4fGzlrGABgB6faAgv5ltXWADWMEXhohYJXA3MXupwuRtVsLF39IhiK9YzNde11uiyAc8ytTUwkYrFnorux/ObWVkfqpF+dHZA8LQePdKQPAiZ4B4DdpFptgbF87UsfqDGMwCNbKFhsFL7WrIdDBY63AmqOtjeWM2JR5HQf+2RF74mtFaJ3AKg+p9oCp9sgoHYwAo9c9TYKD9SJrHN6mls8sXT8bG7QpeXgkbZXRMTj8eQNorfsSY+v56SUFb0z9A4ADnGqLTCbr7W2BqgHjMAjw9vT4xWpt1F4oF5sbmVWDXraOrwtHhFlPyDNmvR4ydPbEj+Jx+NRhk88bR0HW1IdshavZn58Vkoe7SdbK0TvAOAYp9oCk/laaGuAOsEIPDT2D14+88PIVMzkKPzG0uzM9MNQTJmN7vX2vHV6cMD4ovKc23vGLl/o2rt0tT8YEu+ZG+MDSkLKG9IzlluuQh+UKJV6m4iIhIL9IclOIvtuK48F7DraVYPiaes40taRc0V2T6jl4NF0L2lr5YG6V3xiM55oS09/9OzpOHI0NxnJOtg968B3kT0dR/Vu0OQAAKgch9oCk/maTx+oF4zAI8vegctnTI3CbyxdHR0JTmki3VgsNBUcGTU4iJ9/eyg4UnQ3/MqXamN2tD/7bpMJALvU5jPt1r15EmsrRnpCidjKWonLtAe7t7zW5il2LQCgqhxpC0znaz59oE4wAo8cewcun3lodBR+Y/aLYCgmIt4zY5eV0Wl1Y/dYKDi6Pz18XvD2UfV2ZdhdRDaWZr8IToWCwTIewVCpui7MzV3QG73fmP1iKrtMmfslFpqeHewq9FSdnZ1FihWPx8t4KMAWm88ee7THuGlsrRjqsomIJGKPH0uBZNTTetP/1C5VxC7S09OT/+bWVrHe9S5R83VpzT8gdoHqtwVW8jWfPorRrfYNckXrkKOG61JG4JHH+Cj80owS6p65MZ6eW76368L4jTNeEYlNzRi+/ULm9oHxuTHrFUzZpdr47qFydyZ6V+5Xv5TYi/VyCgc4LRF7/ODxypZmn99EYmtt5fGDZ6bWESrJrGnTURN6nJWQdnMiAMAuUeW2wGq+ptMH6gAj8NBhcBR+KRwSEek5nTsivXfgdM9UMCSh8NKFroJj+AVvl67BM97QlMUD3sos1d6B8bkB3Q/2vyb6x5ECbpPYfPbYwLjF5rMHRa9KbMaebZb4nyIRe/yg3P9vSpUDVoRC6i4ghw4dcrYkABxSxbbASr6W04e+dLWvi7bARQjgoctICL/x4gcREe/+ffmf7dvvFYnJDy82pNC2b8Vu3/vmW96pmKXKusxS5aa2sSHr69+9CD98GCpdnuXlZd33lan1zc3NhvLUU8OzgAA4q5yqqZrS1aBbCmwZjQWA6quZqrUeGgsCeBRQOoRffxETEXltv04srA5Xx16sixQPlXVvL4MdpdpYmp2Zngrx114AAAAAuwhr4FHQ3oHTylp0B86F37v/tSrnmLaxdHUkmI7evV6vt6fnzNjYDWUJPQAAAAA4hBF4FNF1YawnFAyJhKZnBy/nflpsQnqxiezZ9Oezq/cbkHtluaVamlGOh+8Zu3EhK4GNFwZLBAAAAACVwAg8iuq6oOwIH5v6YiY3gFVHyXW3ZS82kT11+5tveUUk9vC7jYL35/vhhc7VNpYqtQXe2IXc8L9gkQAAAACgGgjgUUI6hA/lrQnvCvSIiISmZ3OC6o3ZaSUMDhQ7Rj4Vweef65a6X2vfft1T3NQz4+wrVQF6RQIAAACAKiKAR0mpEF7nk0H1aPWR0dklNVzeWLo6OqIexD5YPFJOLbIPBUevzm7k3Z9zsboqPhS8qs0qmB9Wmy+VZlhf/TuBJhfZKFQkAAAAAKgi1sDDgPRa+Fx7By6PvfgiGIrFpoIjU9pPvD1jl/POd9dJ+MaYfBEMxUJTIyHN/V6vV2I5x7ZlFuQHRzJF6Rkbk2BO0YyXSl0wH5sa6Z8S6Rmbu9CV2Xx/JCtV75mxtx4Gp0wdQgcAAAAA9mEEHoYUHIXf23Vh/MbYmR5veot2r7fnzNiN8bw15PrS92tuH7sxfvkt3UJkX9lzZuyG/hH1Rku1d2B87Ez6ImUgfu/A+I2xvDvnxge61NX1uov2AQAAAKDCGpLJpNNlAPJszI6OTMXEe+bGeOlx/F2vs7NTRJaXly2nEI/Hnz59al+JAEB19OhRp4tgSDweV140Nzc7WxKDLNT8NBYAnOKWtqCkemgsGIEHAAAAAMAFCOABAAAAAHABAngAAAAAAFyAAB4AAAAAABfgGDnsSnsHxucGnC4EAAAAAOwijMADAAAAAOACjMADwO7QcvBoxx6RrZUHzzZT73laDna07fF4REQksbW29kNsM1EiHY/3yJE2T97bibXHj2N596YuLvCpNnuj+euUqEJP4fG0vKYtX2Irrpd0sadQvnT9pweAarOrIdBJM7+mM5Nw8cbCAEebs+KZ0xDAZRiBB4DdoOWg2sH6IdNp8x450pEOPEU8e9o6jhzx5ndmsnleKXWFJtfXdPpG6RJlZ280/9ziVOgpPN4jueXTS7rEU2z+sJYQ8bR1mH0sALCdfQ1BXpo675tJuFhjYYCjzVnJJ6UhgMswAg8AzvN42/aIiCTi6UEBj7dD6Y1srT1+FkukBhc8ba+1xDIjM/la9ih9tZKDCZ6Wg0f0+3UiSp9PyX7l8bPNRHocxNPc4okZH6So1FOk000lLJ4Wb0dHm0c8bR3ezdQ9pZ8isRlPtHk8JQsEABVmY0OQVjh8z64cWw52dOzxiKftyMHEg5yUizcWBjjanBl5UhoCuAsj8ADgOE9Ls0cku9uWemvtB6XfkoitbYmIyJ62YoMEHs8rkpOUXn7eg0eKdchS/UjZWnm2mVBzT2ytrTx+YGqKYaWewuN5RRIJbcKS2Ewl7Wlu8Rh/isRmPFG6QABQYTY2BCmFwndNaPyDUjluPlMTlj17WrRlKtFYGOFoc2bsSWkI4CoE8ADgtCLdNvkpke64JH5SXhadVJj6UHNfrpaDRzralBGJAhelc09lKSKbzx4/M7sAvmJPkdh89vjx4wcP9P+coN5t8ClSHbd03A8A1WdnQ6AoGL6nQ2NtwptbeXFt6cbCCEebM4NPSkMAV2EKPQA4TK93o9erSSR+EvGIyCsej4h+hyY12CCvtB052qH2arbW1BHo7OS21laeJV47qtPDS+f+U0K8B48o3SMLWydV4Sn07lK7hkafIlUik6sDAMA+djYEIpIJ3xNraz+1tZkZQ89NuUhjYYSzzVlRmtxpCOAijMADgMNSvRvNQHGa3ntFxizSgw3iSW/Y4/Hs6Thy5KBmUqQkttZWHitLx4vb06HGvWJ1Dzslv4o8RfY93vQcyrzplqWewsSIFgBUhI0NgYg2fF8pVtO/otnaLV3xanM22lgY4GxzVupJaQjgIgTwAOAsvRl+ltNKLQ3fWnn84MGDBw8er2wpo9F7OtJ9HlOT4dWUHqfWl1djm15DT5F1g/aooa21/J5c8adIJH5SXmh6eABQRXY2BKXD9/Qi9HRl6NFs4p4OYa2snLKNPc2ZwScVGgK4CVPoAcBZdv65f/PZ4+wNdBObz9b2KNMK9+xpkU2z2+turaW2f/thrXlPW3XmF5p7iuyDgrdWdLYQNvoUnleKT0kFgMqwsyEwMvq++WxFqVQ9bUeOtmV/pjtUXnV2NWemn5SGALseI/AAsIvpdurM9a7S+/VYGFbQ5JQenbDQz6zcU7QczI7ec48/ys2pnKcAAEeYqkINTp6XzWePV9a20udxbK2srFU2anWsOav6kwIVxgg8AOxCiZ8Syg4/GhanWOov9zOduxUVfoqWg0c6UmvbJbG18jg7erfrKQDAEVaq0PTWb/kDzso7mUPVE5uxZ5uxzMfetqIpW+Vsc6YUoTpPClQJATwAOEs3ztTborfYHkeKloNH2l4Rj8ejHYrWbMZutKuS2Iwn2jzZM83THScTAyaVfAqPVxu9p3ukZT3F7pg4CqD+2NgQGOFpOdjR9op4PD+lq9n0NvhbW2bXWhXnbHNm/klpCLDrEcADgLP0j9PZ/GGtbU+bRzxtHd7Nx7GEeLzqOUCaTdpaDirrAVPx6+bWTx3KAsGOgy2JZ5sJ8aQPAk6s/WC8U5aJfdXsM9v+ZLLPzV1H5Z5Csw1RwfwNPYXYvXsUAJhmW0Ow+exBblWvX1cre7t72ryezVhCPN4OpXY01VQUTDyLo81ZwuiT0hDAPQjgAcBhqZGX7J1zErGVteYjbZ6ceZDFOy7p3XpkT8eRox2ZD7ZWiq+GzCtT7PHKKzr7/ujuEVc0nYo8Rbr7J6I3UTQ1YmPwKewZ0QIA62xsCIxIBdV5CZtrKoxxtDkz+qQ0BHAPNrEDAIclNuNKfyFnW55E7PHjla3MWIBy3G2JjsvmswdZN0kisbXy+MEz8ycB5aSUSsdsMpV4ivQEyNIMPEVq3CXv/HgAqBZbGwIj+cUea7d2UypHOxIunJtDzZmxJ6UhgIs0JJNJp8sA1LjOzk4RWV5etpxCPB5/+vSpfSXCbpM6Ca3IHMRdquXg0Q7R3/vdLdz75dvj6NGjThfBkHg8rrxobm52tiQGWaj5aSzqm3vrIhqCWuCWtqCkemgsGIEHAMelhl48zS1u2jXd03KwbY/tGx5VmzpvkmEXAI6iIXAQDQHchAAeAJyXiK1tibir4+bxdnS88lPu2W1uk1pNn7WrHQBUHw2BU2gI4C4E8ACwG2z+sJYQV3XcErHHDx5bWFu/q6ir6cvfEQoAykZD4AgaArgMu9ADwK6QiD1+EHO6EHWHbx3ALkKV5AS+dbgMI/AAAAAAALgAATwAAAAAAC5AAA8AAAAAgAsQwAMAAAAA4AIE8AAAAAAAuAABPAAAAAAALkAADwAAAACACxDAAwAAAADgAgTwAAAAAAC4AAE8AAAAAAAu8DOnCwDAkEOHDolIc3Oz0wWxUzwer7EnEpF4PK68qLFH48dykZr8sWAQjYVbUP+4CD8WdhVG4AEAAAAAcAECeAAAAAAAXIAAHgAAAAAAFyCABwAAAADABQjgAQAAAABwAQJ4AAAAAABcgAAeAAAAAAAXIIAHAAAAAMAFCOABAAAAAHABAngAAAAAAFzgZ04XAIAhs7OzThcBgIudPXvW6SKgGmgsgHpGVV8PGIEHAAAAAMAFCOABAAAAAHABAngAAAAAAFyAAB4AAAAAABcggAcAAAAAwAUI4AEAAAAAcAECeAAAAAAAXIAAHgAAAAAAFyCABwAAAADABQjgAQAAAABwAQJ4AAAAAABcgAAeAAAAAAAXIIAHAAAAAMAFCOABAAAAAHABAngAAAAAAFyAAB4AAAAAABcggAcAAAAAwAUI4AEAAAAAcAECeAAAAAAAXIAAHgAAAAAAFyCABwAAAADABQjgAQAAAABwAQJ4AAAAAABcgAAeAAAAAAAXIIAHAAAAAMAFCOABAAAAAHABAngAAAAAAFzgZ04XAABQIU2+42+/3t7YqPxrZ2c19v3yo+i2qTR8x88eay/88er9m/ei5eXb5Huj83VvmcUEgHplR1WfS1P17zyZuf0olVrTG+8MHm40kEBe40BVD9iFAB4AalF+L6uxsb3xWLu3/f4/7xnvMjU1GempWc5Xpy9oqZgAUJdsqupz+I4X+8OteVT1gJ2YQg8Atcd3vNAYSWP7sbffaDKcUNPPTQXwpvItNpLT2H5s8LjPTNYAUG9sq+qzNL3xup3hO1U9YDMCeACoNSUGTxoPG+7XmRuAN5Vv0xtvl5iH2X6Mfh0AFGJfVa9Vum42YOfJctRoclT1gElMoQeAGuNr1/TpUqsXm3zH3z7WrnajGr2+pkePDMxa1AzA6652t55vk8+r6dOt3p9RZlE2+d54+1i6t9f++htNUSPlBIB6Y2NVn1Eq3t5+dPvmI/3iZFbNr95PL5qnqgfsxwg8ANQWbadu58k/1U7RdvTe96vp9xu9PiMDM5oB+J3/lOpcmctXOzd/9X56DeR29NHt+6bLCQD1xsaqPs366LtmNsDqfc0fe6nqAfsRwANATdHOet+JaTcHiq5quks/NxTAZ7peO9slAnj78t3+z47JcgJAnbGzqk/xdarh+87q6k6Ja7NvLBC+F0VVD1jEFHoAqCna8Y6cqHv7PzsiqamVTU0ixmPyHWk6/s476gFAusf/2JivltnrAaAe2F/lZqLw1e+X/2NiHztN+J5Z+24SVT1gHCPwAFAvtrfNjXdoOoiN7YfTx/dKY2P74WOD7xw3OudRL1/t2Ev7sXfeSE+t9x3PmsLJuAwAmGK2qheRrPDd8Bi6iGTtWZ+ZyZ8pC1U9YDtG4AGglmRNqyy5bt1wUjoa248NHpfUznZm892OxnYOZ/4kcPjY4OFj1osKAHXFxqpeJD98Nx5Np6fdi6x+n78RHVU9YD9G4AEAugycAV/G8T/bjzQ7LQEAnJIZRDc7BV57ZPzqqt6tVPWA7QjgAQB6sgbgd1bvz9y8efPmzZsz959oNzdqb7d8gG/03sx93Y2SdnZMbZ8EALAss/W8zhT44jTD74VDf6p6wGauCOA3Zkf7+/v7+/uvLpW8aHR2o3oFK8vG0uxV9bky5dZ9sxJ5L2WlvHTVXd+dvXK/DcDVLC1+1BeNPVld3dkRkdX7twud/qNsPWQx3+3ovdsz959kunY7O6v3Z27+M5a5xIa5oQBQa2yr6ssI37Un2WVvhJ+Dqh6wlbvWwIeCVwNzF7qcLoYdlq6OBEOaf7+2f2+BN222sXT1i2Ao1jM212V/4q7Dt4H6Ym7R5Pb2o0f35JHeR9HV1WOpjlvjz0vuHVw03+3oo3vR7Fx8pefuAwD0majqNQe/661gL85o/C4iVPWAjdwVwNdOCL8UVgL1nrEbF9Kho+6bdlsPh2J5b3ZdmJu7UKEMdzXdbwNwtyIHCBU5dmg35Zs9d9/ucgJADbClym3yedOXth87e1Z3f7nGw4NnD4vI6v2bWdvTa+J3SzU1VT1gkdsC+NoJ4UVEpCegE6jrvgkAxmxva3p1Xl/To/SgirkBeN/xd15vFJHGxkbZeTJzWzs2k9VvU1Mym2+T7/jbqQyy0tf2KAvsigQAdc6uqt4ybTZFamqqesB2rlgDr/L29HhFRELBYmvhAaC+RVczK9QbD7+tHrvb5HtDc+hu6emOsv0faWxsVE7/aTz89vHM6b1vvHNME7+nUzKbb1NjYyqDxsOdavJNvuODh+nUAUAJNlX1Vmnj76J/JqCqB+zmqhH4/YOXz/wwMhUzOQq/sTQ7M/0wFFPmSnu9PW+dHhzIHuReutofDIn0jM1d2Ld09Yvp9MU9Z/KuLTM7NStFKNgfyksg9WbPWPoZDTyCeuHG0kzB4muzVjNR81A+8Z65MT6wVzZmR0emYql/5eUwOzoyFcv+0HDx9OXc3jN2+ULX3qwiaUqv+VayHyvvAxM/RPa3oXO3lccCHKNdoa5/7G52p853/Gw6Is/Mkcw5vbf92KDe7Ertqklz+Wanr588nToAKMCGqn770e2buhudNL3xTjrAzp2CpV5hcJ4+VT1gOzeNwIvI3oHLZ0yNwm8sXR0dCU5p4rBYLDQVHBnVvz18tX8kqL04NBUcMTPeby47m9PcmB0dKa/4IiKy9823vCISe/idzt7sSzNTMRHvW29qAuGyHjn/9lBwpOh5A0aY/N1z754d7c++22QCgMOi9+4XO3fX4FbD24/++aTEGT+r97NWRJrLt9TpwDtPZu7RqQOAAuyp6q0xPk+fqh6wm6tG4EWUEP6h0VH4jdkvgqGYiHjPjF1Wxk7VbcdjoeDo/rwR5lAolBoAVi4dCYZMrLrfmB1Vs8uksTE788VUSJOdsl+c7qix3puG0lTvHpnKulAtf6b4XRfm5i4UHMjWfMdvvuWdisViUzNLA7mj3eGQaON348Uz/I0tzX4RnAoFg6W/7yLJGvjdC34bG7NfZH+RkrlfYqHp2cGuQk/V2c3oFJAAACAASURBVNlZpFjxeLyMhwJMid6baXpHO0ExY/W+4T7d9qPbM1IgGdlZvf/PvF6XuXyLXK0/5FPfRkZGnLp9a2urnKztVfN1ac0/IOxjT1VfaVT1ZTJVe5ut6ndV9W6vGq5LXTYCL2JmFF4ZLVamYacCsb1dF8ZvnPGKSGxqJv/2nrHxC5pLx3pEROSHF0bOCddkl0lj78CF8bGeQtnZmKa6hb3mAdLlD02bPOBdHYOXUDinyEomPadTAWyZj6x7e9fA+Jz6vVtk4XfX2PjuoXL3Ze1RAHu7Lqj/2cVerJdTOKBath/dvjlzXznGXbGzs/rk/szNe6aWRCrJPNGmoyZ0Wz8hc/luP7o9o3s1XToAKM2mqt40c4eLUNUDdnLdCLyI4VH4VEh7One8dO/A6Z6pYEhC4aULXdq7vWcGsxPbt98rYuycsY3Z6QLZSdfgGW9oKpaXnZ1pph42kJVBV6BHQiEl5jSzdrvAN6TG76k8yn3kgj9Q6nYTJTaSbMHfPeeq8bkB3Q/2v2b0PwZgl9iO3rttYGZi9N7NolfpHN9rS77qxaaurmc3btwo5/aBAd2qDYDb2VTVZyVZaHG8lcSUFKnqLTNV+VPV1wNXBvDGQviNFz+IiHj378v/TA3Mf3ixIdpNyV7bnxvyKTGbkQB4/UVMJC+CzkomN7uSjKdZ6GEtH/Cuhv7aWFeN1zOlKfORi/1A6ix+KyW38LsXS21jQ9bXv3sRfvgwVLo8y8vLuu8rU+ubm5sN5amnhmcBAaiOcqqg3SBdDbr9QUqisQBgmakKJB6P116NWg+NhUsDeCMhvBpe5gXlIqYCcy11f/Zc2hXUuvvKq0wPhBtPs9jDWpMXwatTy/PD9TIf2cYyi4g9v/vG0uzM9FSI4XYAAAAAu4gL18Cn7B04rayU5lz4iugKKAvo1XXwSvyet8igUvbuf60q+ejYWLo6EkxH716v19vTc2Zs7IayhB4AAAAAHOLaEXgRka4LYz2hYEgkND07eDn302LTpYtNsy6i4PLojGK7u1tlIE3zc8NLyxqDzz8+zkzxitEvs/oLGZB7Zbm/+9JMUN0O8MaFrAQ2XhgsEQAAAABUgotH4EWUEF5EJDb1xUxueKWO4epuGm7/jHMlbjS6Yb3taRZ82KWr/f39/dbOVdeMweceH2eyeLqKnjev/kL5SuZW5u+e3s3/Qm74X7BIAAAAAFANLg/gNSF8KG/Fcir+zDtELb17ut7ua1alwlG9Q8o2Zkf7+/v7R02e5mYmzZwJ7ylqOGp2qoGqa/CMV0RC4as5x8eZLp6uwvenfiEt9c8FuYG5emacttQV+d31igQAAAAAVeT6AD4Twut8Mqge/D0yOrukBnMbS1fVjejsXs6dWpMfCvZnstNmqHNcmo1ppoLt4OhV9cKNpdnRYKGD2owNm6shdiiUvf28heKVeLzRq7Mbeb9QzsXqqvhQ8Kr2xwzmh9Xmf3fNt6H+nUCTi2wUKhIAAAAAVJGr18CnpNfC59o7cHnsxRfBUCw2FRyZ0n7i7Rm7bDqcLl2OG2Oim52I98wNSwvFjaeZ2pc/FgqOaL8Lb8+YNmd1iXhsaqR/qvT6dc1xbrrj1uU+cvr+0NRISHO/1+uVWM6xbZktD7QP2DM2JsGcH9/4767zbWSON8j6GsV7Zuyth8EpmzcaAAAAAACjamAEXqTIKPzergvjN8bO9HjTG4h7vT1nxm6M561wtkV+dmp+c+OW/1xgPM29A+NzN8ayn3Xsxnh2FL13YHzsTPqS0ivKlTH4wvMVyn3k9P3Zhb78ls61XReyr+w5M1bgbwRGf3e9b2PvwHjut6g8Tpe6ul530T4AAAAAVFhDMpl0ugxAno3Z0ZGpmHjP3LD+p4/do7OzU0SWl5ctpxCPx2dnZ+0rEYC6c/bsWaeLUJZ4PK68aG5udrYkBlmo+WksAJTJVFUfj8fdUqMaVw+NRY2MwAMAAAAAUNsI4AEAAAAAcAECeAAAAAAAXIAAHgAAAAAAF6iJY+RQe/YOjM8NOF0IAAAAANhFGIEHAAAAAMAFGIEHgCryHT97rF1k9f7Ne9HUe02+42+/3t7YKCIiO6tPvl9+FN02n6bsPJm5/Uh7Y1bCsrOzGiucdNMb7wwebtRJxCgrT5HKNEd2GbIT3ln9/p/3MgkXSCFF+Z6VL8jykwGAedWs7c3XpSVahKIqVtubSVm/zaK2R11gBB4AqsZ3XO17LWf6c2+8M3gs3aUSaWw/fGzwnTeazKaZJzdhaSyWtK+zSBhcmsWnaPp5qUzzEm5sPzZ49rjPVOmiy092RBoPv23iawWAMlSztrdSlxZvEUwkZFttby7lAm0WtT3qAiPwAFAlTW+83i4ishNLDyk0vfG20glZfTJz79F2akyh8XCn71Fm0KaIwuG7mnBOynpJN/mODxbqFhpi9Sl87UquBUdL0gmrV6Qeov3YcV/0XlREth/dvvko96bUQE+667wdje0cbmw0/rUCQBmqWduLmbrUQItQ4sEqVNubSblYm0Vtj3rACDwAVEeTz6uElZoeXeqtJ8tKl2b70ferIiLS/rqR8YPS4zH5KUt7e2b8usn3xvF3yoverT9FU5Naxlihaf1NsrOzsyOy+n3uQzQ2FUhY00+9n+knbkdjO6ULBAB2qGptb6AuNdoilFSx2t5oyqXbLGp71AECeACoiiI9OtnZTvdptv+zIyIijT8v2fco1qErYuc/qcx8xwePHW5XSrVjISWF5adIdym3C/TotqOP7t2+fftmZgVpqhdYMElN+J41+JLq0zV6ffTpAFRUtWv7knVpMZkWobTK1fbGUjbUZlHbo/YRwANANeh1T/QWBW5vq92SgmPMqlSHbufJk9X8T6OrypuNhzuVUYjUjE6dEZCd1Sczt7/XScQYq0+RmlIpja+/c1bxzjvHi/W4mtKrHgsM42Q+16w7zS4RfToAlVXl2t5IXWqmRSiicrW9qZRLtFnU9qh5rIEHgGpID0DojHbojoA0/rxJpGDHKtOh++ej7c7D+RdE783I8bePtTe2Hx48m/p8Z/XJP+9p1x9mdvk1ty2cHpNPkRlMb2zMvGo/NvhO+/3beUsXNTsY5z1E5ppUj/TJP/M/3/7Pjkhjqe8VAMpU5dreUF1qrEUwqnK1famUDbVZ1PaodQTwAFAF6Q6MpTmOubI6dAX7MfljD42NP29qknQJovduO7fHT9PPZWdHGht3Vu8r58I1+Y6/fay9URozW9Tpamz3dvqi9/JGjTKjXrojStvbSpdOGcyhTwegMqpd2xutS0u2CBVjubbPZbDNorZHrSOAB4AqMHCEjmHZHboC+aVHrFfvz9yLbqvb9ja2Hxs8Ljd3w968eT2x7ei979uVM47b230SzfpU3W2+yFOkp8+ndrwrhEEZABVU7dreSF3qbItgrra3EbU9ahRr4AHAabqb/xTaWchIhy4zGr16Xx2q3laOx5XK7c1r6in0pdZpFlxMWfgp0mssV1d3wV8nAECP/bW9rpy61O4WoXK1vQ0pA7WPEXgAcEpqoZ5WqdmXmc2ANCsZte/sPJm5/UhvDWZmVqG9gxJWnqKAUpvMS8GnSN9J/A5g96lcbV9i1bnyT5tahMrV9jamDNQ+AngAqAK93onuQr1i2x+Zp+2ZZTpD9o5mWHsK3/F3Xm+UxsbG1fuaU+J+ntVfS10jml6q/lNk1r+XfjYGcwBUUJVreyN1aUq5LULlavvKfD/U9qhRBPAAUAX6m+pEl5+83n64URoPv/1G9Paj7cxG6pqF3L7jylJBZbAleu9m7hBz9gVKdqurx9rbRaT92HHftrLiscQRbAXoJJ7LwlOIRFd3jh3LKWHqpOPUIXDb/5HG9kYROfz2G9v/fBTd1pzznv0UBXqrWgzmAKiGqtf2pevSqKEWwcna3ljKBlHbo9YRwANANeifa7P96J9PvIOHG3OmSOYfY25W9N799neOtTeKtB8bPHss84H5xZSlWXqK6L37qU2Msku4ej9VwnS60nj42ODhzDU7q/ez+pdGhpLsndoAAAU4UNuXqEvtaxEqVdvb+v1Q26PWsYkdAFTDdjSm7BiUsz/b9qPbM/dXd3ZS/95ZfXK/4OiHGdF7t2fuP9GmbFvS+Sw9RfTezaybZGdn9f7MTe0BcduPbudes/rk/kz+OfGpT0sOwJubfwAAZjlQ25esS+1rESpV29v3/VDbo+Y1JJNJp8sA1LjOzk4RWV5etpxCPB6fnZ21r0RwROocnyLTE3cp3/Gzx+T+rjh8zir3fvm2OXv2rNNFKEs8HldeNDc3O1sSgyzU/DQWtcK9FQ61veuZqurj8bhbalTj6qGxYAQeAKojNSrT6PVV5Bi3CmnyHX+93fWbu6tTKhmRAVB51PYOorZH7SOAB4Aq2X70/aqIu/p0TW+8faxx5/6Mm8djJL0bkvnNkADAPGp7p1Dbox4QwANA1USXn+yIq/p0249u37x9z+UDGeohc+VvFgUAxlDbO4LaHnWBXegBoHq2H92++cjpQtQdvnUA1Ua94wS+ddQFRuABAAAAAHABAngAAAAAAFyAAB4AAAAAABcggAcAAAAAwAUI4AEAAAAAcAECeAAAAAAAXIAAHgAAAAAAFyCABwAAAADABQjgAQAAAABwAQJ4AAAAAABc4GdOFwCAIQMDAyLS3NzsdEHsFI/Ha+yJRCQejysvauzR+LFcpCZ/LBhEY+EW1D8uUqs/FlyKEXgAAAAAAFyAAB4AAAAAABcggAcAAAAAwAUI4AEAAAAAcAECeAAAAAAAXIAAHgAAAAAAFyCABwAAAADABQjgAQAAAABwAQJ4AAAAAABc4GdOFwCAIb///e+dLgIAo6anp50uAuoUjQXgCjQTsIwReAAAAAAAXIAAHgAAAAAAFyCABwAAAADABQjgAQAAAABwAQJ4AAAAAABcgAAeAAAAAAAXIIAHAAAAAMAFCOABAAAAAHABAngAAAAAAFyAAB4AAAAAABcggAcAAAAAwAUI4AEAAAAAcAECeAAAAAAAXIAAHgAAAAAAFyCABwAAAADABQjgAQAAAABwAQJ4AAAAAABcgAAeAAAAAAAXIIAHAAAAAMAFCOABAAAAAHABAngAAAAAAFyAAB4AAAAAABcggAcAAAAAwAUI4AEAAAAAcAECeAAAAAAAXIAAHgAAAAAAFyCABwAAAADABX7mdAEAoDa0db//u191trYq/3r5cnn5H3+/tbhWVprd70+f61QTnPvw4q1SqZm6PnPx8vXTVxbLKigAoBSHmom27lP/86tObb7/+PPfF9cK5Ztzubx8+fKlDQUFYBcCeAAoW9uprz7rb9W+09ra2X+us7Pr+p+vWO70dL+f6pZV4HqziQMArHOqmeh+/6tznbn5nvus81fL1y/m/+U2v5Qira2trWUXFIB9mEIPAGXqfj+vw6Nq7Tz3u1Nt1lJtO/UrUxG2mevbTn1F+A4A1eJQM9F2Kjd61+b7VW6+hUsp5RUUgJ0I4AGgLCUGQFr7LXV52k79rnA/qqzr2069nz/CAgCoFKeaie7/KXpBa///dGelp/lzwMu56x+ePn369OkPry+/LHADAGcQwANAObq7NP2yl3P5PR5p7XzTbNesMuF7W/ep97+a/qy/wHgMAKACnGomtPm+XFYC8g+vz2Wylc4uTUC+L73qXV7O/Tm15H1t8co/lvVvAOAMAngAKENWB2nuz7f0ejxmu2aVCd+73//sHLE7AFSZU82EJt+Xc6nl62uLtzTZFvLyhXat+/rLlwWvBFB9BPAAYF3b/kwX6uXyd5ouz+KSpmvWus9EmulJjy+Xl430msxeLyIv5z68XroLBwAol2PNxPrS9bnl5ZcvX+YG5BkvX65rrs/E6a37tX9O0A7Na28A4AwCeACwTtOvye0haccssjtDRWXWSi7/4+8G4nGz18vLuesGDqQDANjBsWZibXHx1pUrFy9ePJ05KLStW7uF6fI/tG3B2nfpPwa09v/uVHebesf7hW4A4AyOkQOAilh78VJE7be1tu4TMdLt0fTLrl9ZlFO/svP6l8tzHOULALtFVZqJzH3TWVvpvZz7MOcYubVbFz8UdZPT1v5zn/Wfy/o4/wYAjmAEHgAsy5oaWf7Mwpx+mb3XL165eIXoHQCqyuFmQrccIi/nrv9ZbzB97dbFD+f0ZuW/nGPqFrBbEMADwO6QOcLn5dzfDfTLzF4PAHC1Mqp97Ux+ZYB9Ou8ceGk7VeikktZ+vesBOIEAHgB2g8yewpldiu28HgDgamVV+4tXTis+nMusdP8sKybvfv+zzNb2y+lz4AteD8AZrIEv39LV/mCo9GXeMzfGB/Zm/r2xNDszPRWKZX2q+6b9NpaWpKsrk7LyCBXLbpfL/TYA4ywtYNRD+A4AtcixZqJggW5dvN6aWg7f2v8/3beUqfiZ0X11uXvquLtbF0XvegBOYQTeIUtXR4LpQF1EXtu/t8CbNttYujraPxIMcwyICN8GKsrEwkfNib6G9vg1ez0AYPepYDNRlPb8us6ubuWF9rC4rNPuLO+WD6AyGIG3Tc/Y3IUuoxcvhUPqTTcupMd+dd+023pY8xeClK4Lc3MXKpThrqb7bQAmrL/UjK3sb9OOrRQ5OihH25uZFYed56anz+ld1Nr/2XS/iCxfP/33/eauZ7QEAJziSDNxZbGt+9T/dHW2toq0toqBHeiyN7nLZtssAgB2YATeUT0BnUBd900Au9LaC824ROebmoEJm3ceBgC4kUPNxJq0dna2tra2tuZmK9LdlTlPTi/j1tZ9Wf8uFtwDqDoCeAAog3YmYmv/7051t4mItHVnZjvmz0YEANQNh5qJrHnv/b97X8lW2rpPfaU9Dz418q/9O0NmXr1CO1NgeYk5XYDDmEJfbVlb3oWC/fnb36Xe1MzJ31ianZl+GIop07293p63Tg8O6I3Tb2wszXwxnb6w54z2Om3WaiZqHlmb2G3Mjo5MxQptaad8mv2h4eLpy7m9Z+zyha69efvqqaXPX6lQ6IMSpSr8bejcbeWxUC8Wl5bPdaY6Q6395z7rz53amN0x635/Ot13Uqe3r926ePqWXtptp75K7wmcdQiv2esBAE5xpJmQtVv/WO5Pp9Paee4znYn3mtPoskrZee6rU+sXb60pf2jQBPzE74DzGIHf/Zaujo4EpzShZCwWmgqOjF5dyrlwY3Z0ZCSovTA0FRzJu6yUvW++5RWR2MPvNnQKMzMVE/G+9aYmEDZYPH35t4eCI/2mC51jo6xSbcyO9mffbTIB1JfFK9eXi3zMLvEAUN8caiZKZJubcfblrf2fTU9PT09/dq5fM9Hf7OnzACqAEfhqU/aL0x011ntzY3Y0GIpJemBaRDY2Zme+mArFQsHR/Zpx8KWrI1NZF24sXR0JhiQUvBpQEuy6MDd3oeBAdsbeN9/yTsVisamZpYHc0e5wSLTxu/Hi6dK7fWn2i+BUKBg0/I3qJfuFmuyZscsDarJXvwiGYppSFfw2Nma/yP4iJXO/xELTs4NdhZ6qs7NT/wMREYnH42U8FHazxSsf7v9Kc3yuxvJ1wvda8b//+78Vunhra8t8cUqo4Qqnhh9NUfMPWJccaiaKZKs3TWvxyofy/u/OdeqveGdel0EG63+Dl1WigdCq4Qqnhh+NEXjbhIL9RYzO6gxnl6YMeIv3zI3xdCy5d+/AhfGxHhGJTc2kx4PVLex7xtIX7u26MNYjIhKaNpm5OgYvoXDOcLOSSc/pVABrvHjGn65rYHxOKbdVmmQHMsleGL9xxmugVBvfPVTuvqw9CmBv14XLZ7wiIrEX7EaGfGu3Lp7+8PqyZsXhy5fLc9c/PJ06SRcAUM8caiaUbOd08tUNxtcWr+RdX/QGANXHCPxutjE7rUTlp/OGfLsGz3hDU7FQeOlCV5dk4vdA1oh5V6BHQiEl5jSzdnvvwOmeqWBI0skr1Pg9lYeJ4ulKlbng7SZKbCTZAk8luVeNzw3ofrD/NREOnUNha4tXLhqYW7h45bThGYgFVz3acL2ZckBE5P/9v/9n/OI//elPlSsJAHdyqJlYW7x1ZdFEW2L2emQz2FjQTMAyAnjbmDoH3pj1FzGRvKhcoYaTP7zYkK69svHiBxER7/7scz+sH/Cuhv7aWFeN1zOlMV48XYXKLJKexW+l5MWS3bffW6pUualtbMj6+ncvwg8fhkqXZ3lZf7GZMrW+ubnZUJ56angWEFCTyvn/vXzxeNzZAlRCuhqsvUfLQWMB1IPdUJXRWLgUAbwL6O5Vr1IH19VY+rX9tu2RnhfBq1PL88N1A8UrxsYyi4gU/SrUPyuULNXG0uzM9FSI4XYAAAAAuwhr4FFAV0BZQK+ug1fid++Zwf/f3t20NnKkARwvQz7CnOwZHIGdQ9ijwURiDzraC2ZDzDD44L3JGJPINpPAoD3GDCRmLO1ijH1bH8wwzJAgiH30IUhB4GPYQ2Twmol08nfQHvq9u/q9W62S/7/TMFJ310u3qh5XdVXGswx8zD37dCzXkRj0jrYPzOi9VCqVqtXNRuNEe4UeAAAAAArCCLwCIkzOjz83PJxjDN67fVyc5AWRp1mfCB+B+5tBRRE0vd7Qe3egLwd4suc4weBjxBQBAAAAQB4YgZ9kT5+VhNBC3BD6gLV3hfTe0dra2lqyfdVtY/Du7eNiJk8qcL95fSK8V+jVfItCBE+v15mr+e+5w3/fJAEAAADAOBDATzIjxJVtfDZ4X7dvT+ea8G7Qw9HAIWd/y883S0KI6+6Ra/u42MmT8j/eWN/eTv9zgTsw1/eMs6e67LN5nrlqvmzVvTCyJAEAAADAGBHAT7S59Q0tGD1Yq7/vmfHooHdU3z6/E/aY2gi2D+pH+hcHvff1A7+N2qINm+sh9vW1c/n5BMkLyV796P1g4D7a9WX9rfjrAyODYtA70nPooBfF3fm2lSrrrLLX+G2lof+dwHYVMfBLEgAAAACMEe/AZyZoMXZNkpfFl/dOGuKHg+u7u/OD7XPnZ6XNE9v55ta/2/x1+/zu7vpg256QUrVhv6r+ivjd+fbaeXiSbNu5ScetoycvJHvX59vXtuNLpZK4c23btrzXqF4fXAvhyGC10RAHrih+bv27xkdpqkrVxnf2vylISkMvRuEqRlHabPz114PzjBcaAAAAAICoGIGfeHPLe62Txma1ZK2BXipVNxsn7ZZreHtuvdU+adi+WCpVGyctZxQ9t95qbJpfCX+jXBuD919+PnryQrLnTPR3f5V8d3nP+c3qZsPnbwTeVGmJarnebJeVxtx6y12KWnaW9bfrpS/tAwAAAEDOZkajUdFpADwG7+vb53eitHkS7e8Ak21paUkIcXNzk/gMDw8P33zzTXYpApCvi4uLAq/+8PDw5MmTAhOQh4eHB+0fqmQtwS8/jQXweBTbTGhoLCZBgl9+RuABAAAAAFAAATwAAAAAAAoggAcAAAAAQAEE8AAAAAAAKIBt5DCR5tZb7fWiEwEAAAAAE4QReAAAAAAAFMAIPICpVtm/2FkS4uZ4403H+L/5yv7XXy7NzgohhBjetH/68LZzH/+cYth+9fKt7cD5+cpX1onFcHhzIzl12qvnnQv3qWXnDsmpdmrJiQFgstBGxM7Fi8PXa7Oer+tf8/nUoJUzbQSQCiPwAKZYZV/vf3ywemYvDl/vmB0oIWaX1nZeH76Yj3tOt/kXh68dJxazklOnvnpW5/HJhRCisn/oyofr3OE57XxoD4WYXfs6fsYAYHxoI/z4txFPZ/3j84hoI4BUCOABTK35F18uCSHE8Oa3e/O/vtbGBm7arzY2NjZetYdCCDG79lUl2jl9u2b6eY0Tb7w6Nk5tdVFSXz3nXOifaKc+fmWdWYjZpS/mo+f0/rebYZKMAcD40EbEzIUQQlSWtU+Gej4M+mD6/duXGy5mO2L9oYQ2AkiDAB7AtJr/QgtF7X0z47/aH4y+xk83Qgghlr6MMhLg16mZfzorhkP7icV9xzi1FfumvLonYxnnwurOipvjN517/czDm/bxK6NzFi2nRu8sZsYAYHxoI2LmQggh5p8Zc/J/izb73fani2NrxjxtBJAC78ADmFIBfTMx/Gj2PP4cDoWYFWJ29qkQwf0R/07NfefNy478I2GeOuXVTbnlwnbm4Z/G/7lyFimnQuudra3NitmlL+bf3vOWI4BJQxsRNxdCWBPobecOTIstfH/jaEtoI4DEGIEHMJ1kXRjZq3v3H41J4s+CxwGMTs2w3b6JlAJjoqERDqe6uk1+uTDPPPwoXuwfXlxcXFxcHO6/qISkzZ1Te4qsUXkAmBy0EfJEBefCTPPsl3oTcXF4uO/bRlS+0sN3+yoDzhTRRgCxEcADmE7mMIEVU5pk/6cNTPiyOjX/fvsxwuXnXxwagxjumYYJri6VYy6Wdl6v6X3b0LWP/HL653AYIUUAUAjaCImwXBgT6IWYnbX+tbTz+nBf8jK7+VLWsP1v73LztBFAUgTwAKaS2c2IOM8vmL1TE+F0jn10bn6alH1y4uRiaCxip7+j6bNacEBOkwwcAcB40EZ4hefCfJVfayE2Nl4da++yzy7teEJ4a46D9H152gggKQJ4AFMpg41uTPG6Zs5dcF2v/RUoVi5ufjIWsfvQ9p3mGDGnDK8AmDi0EW5RctF58/Lly42NjZdvOsZSfG/09fHE0rIzgjenz4f9hYI2AoiJAB7A4yPtLkhnG4qYXbPKvrNntiHpmsW6eoD8ciF7l919uSg5BQAV0UbE0OnpEbxzJN18W/6mR+MAZItV6AE8HsY6vHZh8yitJXvWXl+sOT7S/mfYfqVvjVPZP9wx3hwXw5vjl86eWZKrjzcXsjNLheQUAFREGxEhF27We/HS/yV+BzLHCDyAqWQsj+Mge+UuaCGjOOZf2Htm7VfemDarq+eXC2NnXvt8eavbZ5w6PKdOaUsWADJHG5FAZf/wbxVNggAAFx1JREFU8PDw4uLC/r67bfsSK8CXbUrqhzYCiIkReABT6f6jPgQx+2ze2vW286H95dLarJhd+/rFby/f3luL5Npe0qvsX+wsCaEPOHTebLh7Wc4v6P/31Zq9ZyYdqEhwdWne8suFsTOveep571uMUXIqRNZLRAFAlmgjEuTivjfc2VkSQizt7Ff+fNO5F/PmrvHOjeKkUb0TbQSQFAE8gOlkTCKcnX1qdc7E/dt/t5der826pgl6t6iNx+wdCSGbgWi855jV1XPKhRDi/u3L49mLnSV3Lsx1liLmVGQ3bAUAOaCNSKDz5nj5Qg/hX1/s2HPgeHXeO3XLizYCSIop9ACmkzUd3LlDzf3bl6+Ob6zJk8Ob9nHAOHIk5mTB8FRldPU8cqHrvNmwn3o4vDl+ZUbl0XNq9N/k+wcBQLFoIxJxtRBWGyE/dfgAPG0EENvMaDQqOg3AlFtaWhJC3NzcJD7Dw8PDN998k12KHgljr57Aid4TqbJ/sSPUXtdd3cLPxsXFRYFXf3h4ePLkSYEJyMPDw4P2D1WyluCXn8ZivNT9maKNmAbFNhMaGotJkOCXnxF4ANPKGF+R7GA+yeYr+18uKb9wrz45krEVAJOKNqJAtBFAcgTwAKbW/dufboRQq3c2/+Lrndnh8SuVR1as9z1tCyYBwIShjSgKbQSQBgE8gCnW+dAeCqV6Z/dvX2689HubUBX6+54ZLKoHADmijSgEbQSQCqvQA5hm929fbrwtOhGPDqUOQA38WhWBUgdSYQQeAAAAAAAFEMADAAAAAKAAAngAAAAAABRAAA8AAAAAgAII4AEAAAAAUAABPAAAAAAACiCABwAAAABAAQTwAAAAAAAogAAeAAAAAAAFEMADAAAAAKCAT4pOAIBI/vWvfwkhnjx5UnRCsvTw8DBlORJCPDw8aP+YsqxRWYASaCxUMa2/P1QWkDdG4AEAAAAAUAABPAAAAAAACiCABwAAAABAAQTwAAAAAAAogAAeAAAAAAAFEMADAAAAAKAAAngAAAAAABRAAA8AAAAAgAII4AEAAAAAUAABPAAAAAAACiCABwAAAABAAQTwAAAAAAAogAAeAAAAAAAFEMADAAAAAKCAT4pOAPBYLC0tFZ0EAMCko7EAAARgBB4AAAAAAAXMjEajotMAIIQ5IHNzc1NsShCKylIIlaUQKisKSkkhVJZCqCyFPIbKYgQeAAAAAAAFEMADAAAAAKAAAngAAAAAABRAAA8AAAAAgAII4AEAAAAAUAABPAAAAAAACmAbOQAAAAAAFMAIPAAAAAAACiCABwAAAABAAQTwAAAAAAAogAAeAAAAAAAFEMADAAAAAKAAAngAAAAAABRAAA8AAAAAgAII4AEAAAAAUMAnRScAeNQGvaMfLq7v7oQQQpRK1Y3v9pbnxnY4YklV2oPe+3cXvxpHa4c/X56jtnKS4aMxeF/fPr8T1UZ7bznDFMKU+mfQ/mxN7e8gjYVCaCwUQmOhEBoL08xoNCo6DcAj1TtaO7j2/G/kn/6UhyOWNKWtN+pepc2T1rqijccky/LRMCuPJysf6Spr0Dvalhw+dbVFY6EQGguF0FgohMbCjin0QDEG7+sH10KIUrVx0m632+2TxmZJCCGuD+rvB3kfjlhSlXbvSGvTS9XGiXZ0++Rks1oSQtydbx/18k78Y5PpozF4/4O0N41spKwss0NW2pzm30EaC4XQWCiExkIhNBYuBPBAIXrvtHZ605y/M7e83jrZ1Frqd2ENdcrDEUuq0u51r4UQotpo7ZmTIOfm1ve+09uOCyWbjsmV5aNBjyxn6Spr8P5C75CdtNZtxzeqQghx9+tvU/Jk0VgohMZCITQWCqGxcCOAB4pgNNQbzjlxc+sbVSGEuO5Ga+cTHo5YUpW2cXDZPUXLOPzu45/ZJRVZPhpaj6FarWaZQFjSVdbgt1/1Dp1rZvHyc61Tp2SnzIvGQiE0FgqhsVAIjYUHATxQgMHH/wkha6jFcrkqhBD/+xj0a5LycMSSrrSX99rtdlvNV6wUlOGj0Ts6uBai2tgrZ5c82KWrLKNL9tcvPC8Gz6232u12ezpeGaaxUAiNhUJoLBRCY+FFAA8U4M+PIXOtgv/SnvJwxJJXaestErKUWWWZPTI607lJV1n60Z8+U6/jFQuNhUJoLBRCY6EQGgsvAnhg/PTWuPTsqfezp89KQgT/OTHl4Yglr9LW/yYs+5MyksqqsrQeWWnzOXWTn3SVZT960Ht/VK+vaer1o95gen7+aCwUQmOhEBoLhdBYSLAPPACMnbHiDc3+BDJ6ZO7X5TBBzDEV4dp36+7u+mD7mk23MC1oLCYYjYUCprSxIIAHgDHrHRk9Mpr9iUOPTCXXB9tClKqbG8+1pYUHvfc/HJzfibvz7aNnvE0M5dFYTDAaC5VMW2PBFHoAGKfB+/rBNT2yCaXtNkPVKKTaaO0ZGwPZdhZi0y0oj8ZiktFYKGeqGgsCeAAYm96RPoVL1Vlb002frOreqgaTSzKv2Nx0S8WtgQAdjcVEo7FQzpQ1FgTwwPjNPfvU97MIy2WmPByxZFfag97RmjGcQocsF+kqy+iRsZrwWKSrLH3lIvlXjA+nAI2FQmgsFEJjoRAaCwnegQcK8PRZSYi7u49/CiH/zZEutpnV4Yglm9IemKunVBsne8t0yPKRprKMpZ6vD9auJR9r/013Ojupnqy5Z58KEbKz0FSgsVAIjYVCaCwUQmPhxQg8UAD9z4nX3Z77k173WoiwP9OnPByxZFDaZoesVG206ZDliEdDIekqSx84kRw9XWPLNBYKobFQCI+GQmgsvAjggSIsl6tCeFfO0BZFCd/uNeXhiCVtafeOrDcZmW+XsxSVNbfeass0qkIIIaqNdrvdZkQlQ6meLOPlRe/yQ1P2O0hjoRAaC4XQWCiExsKDAB4oxPLzzZIQ4u58+6in/aIMeuZf3sO3e015OGJJVdpmA9GgOR8HHg2FpKss6+i6cfhUVjaNhUJoLBTCo6EQGgu3mdFoVHQagMdp0DvaPvC+P1VtODekNGbUud+ning4MpG4snpHa5Lj7HhRLmspnyw3vQp5snKRsrJ6R/o+W06lamOqBjBpLBRCY6EQGguF0Fg4MAIPFGVuea990qiWzCUwS6Vq4yTy737KwxFL0tIefPxfvgmDF4+GQlJW1vJe68R9+GZj6qYf01gohMZCITwaCqGxcGAEHgAAAAAABTACDwAAAACAAgjgAQAAAABQAAE8AAAAAAAKIIAHAAAAAEABBPAAAAAAACiAAB4AAAAAAAUQwAMAAAAAoAACeAAAAAAAFEAADwAAAACAAgjgAQAAAABQAAE8AAAAAAAKIIAHAAAAAEABBPAAAAAAACiAAB4AkKXbVmUmRKVSqWy1rm5vi06rEEJcbemJatmSY2Zi6yr9FW6vWld5Z1Wai4hub69aW5VKxVlFW1v5pFpSGpmWttByY89JFmeNdf18MzhpAnMXVBpJbtbgFAT95Gy18n8MAWAcCOABAGPW7Xa7Z7uri4uVray68BPq9mqrMrO4+u6PohPiQ0vf4uruWbfbtX/Q7Z6d7a4uzsxUMox6xlAat62Klhvzf5z5ytekV/d4TVJpdLvds93d1cWZqf/JAfAIEMADAIrSPdtdnMpBSd3Vj6tnY4wf47raWgxNX3d3NasqGkNpXP2467lC+fPFfC9qu/pEV/eYTWZpdM92F8c9JwMAskUADwDIRe1y5KPfv6yVja+drU5gd3qh3tGSerpSdFJyc7W1eqb/s1xrXvb7jhrqXzbNOjpbzWyys1RmpX37x+/6v8pNMzud+kLqFKY03bfTBOXOVu3OHxz77Sy6ed/QAJArAngAwJgtLKycdvpNMz78efIi+Kl32/peD9/LzX7ntL6yYA9zFxYWVuq2Ouru/qhUHdX+WXzUjomh384j6zenu/sPQngAqiKABwAUYaH+z5r+TyL48ev/V5veXG7+xz/WXaj/xwh5qCMob6HeuTR+dFT7mxQAmAjgAQDFWPy87P1PfTn1SutWiNurlrmceKWy5VxN7fb2yrF0evjC9rdXra3KjN/5nF8NXDZcu7TryrJsGHPUu7uLPqfLNRcZWPjbc0klWYnxLmBfkRRHSGkElLa7fHwKSL9rFo034M9Wja+7xlkjJliShjTVHXo7udfN97kHXOeRHuefD1mSfZaCNz4M/lTPjSd3UW9+I1ehxZuhldPLkL8bRn8kXUXovE/c2XA8t2EPbvRbwpXs8GKM+gOb05MSugVBmv00gMfD7w1FAAASMKep+r8D7/6m/at697rc7JsdbZ3t/VbrSI9y7VL2Gqz8iHKzb1zQ+fqsfyb6tndp3Re2fc2deG9Gx5CLSIUf+YhoiXGkKmJp+JR24AWcyfa5gPNrMRJsOyaL6g64nS59zi+9B2zn8c9M6HPnOpck37bsBHzqm7uopVFu9j1PuZX5SLkIz4uXdUnJ0x3nkbQ/dP6P5si/lqWZjHdL6MmOdJfaEx34A5vjkxJWV7F/yIBHiQAeAJCl6AG82YGUxHrlsrs3aJ4upHcpuXLQEbVanADeN1B0nyI0gB9HLgLZTleuOZewi3d0hOJIEt8GF7Xr2+EBfLwER0pD9OpOdjt5vx8lE7J8BFdhcBjr+dQdv6cI4D0PeUDeI2UlWs6lPzwJHkkr2Gz6VmS52Q98cP1KN0axRL5L7d/2/4HN90kJqSzidyASAngAQJaiBvC2fqJv3FZu+o56CX3tdPN0joErv+FR2xCWe6ArQgAffiLfHr5/fzfHXITw9NTL5VqteRklmLcS4wr+HRlwJca3NCSlbaXNfYHLpt/5g+69JAnOrroj3E5N2wWafveAq8bsWXHcCOkieOdVfAJA2Xit8zzhpSHLRTNuLhxnjJlxn78BJXgkvbUhnBx3UNPnhAluidh3afQf2LyelIDaIn4HoiGABwBkKTSA7/f7l7WyXz/d1n+UdeJ8Bs8CPraFgp5D+n7hQvCYsH/H0yczfkFQzrkIFzRd17O3nCSB8jnAsvhoFC++DbyC7y3m+0GSBGdX3dJ0BdWo36d938gvIB/+jO/L/9BizOqQD1PLXmhJEMBL5oTHzcUoMCQM/Lrshov8SIa8aWAPlCPeQUluidh3adQf2DyfFM8kDtfpid+BECxiBwDIhbmQmMvi4uLqWVdfbcx/FfTy8795///q57PAo8wlqrrvftHWQLr95Z3/cuu2VdbDGdeW7VG28q1+nu5/+9HPVEQuXIeunHZGzk2yDd3u2e7q4qJnMS4tgXonQrrz98Jnf0mWGhtzgcOz772Xj7/xeIIEZ1fdUmaNyre8s/ZoMO8Bh3LzW29OrBUHf/8jygpgxvedl9AzXvv7t1odOPJoJFv6dMZWbn674jlN3FxkIv4j6SC7R/5uxa7ec5qf2ko3yS2R4i6VV+FYnhQj965lBLO9uYBpRgAPAChKudnv+G1i9pfPAuJ3/x6eEfkZHUZjuzTp+UJXWbe5/eN37R+1v0u6tnFiyiJzIaNvkj0ajfr9y8tmreZ4PbZ7tru6GGVJ6Nvb26ur1tZWxVx/PAUrUujuri5qK1pvtbJdnjwwwdlVt5xZo9Lzi5DwK6MQxyhle1SqZ7z8+aLxqS3Myjh+n5hALcEjaedXhxr5M+uV4JZIc5dGTZbI4UmRRvDE70BkBPAAgPEql43p2b7RuxDlzxf9z2DuTCUZ3zd2EnMN3/mdL/JwsblxelDCYikiF8EWFlZW6qenHS2at43Ld3f/IQnhnTtNLS4urq7unp11Pd9Lwrbdl5aC7tnZrhbLz4TvqeUjRoKzr25nSoyoJ8L5cxyINgIpKyrVo6jy878tmJ+aCZi2EMtT+AkeycwkuSVS3KXBh+T9pEgi+Gm7uYA8EcADAHLh+wZrp9PpnNZXFmJ308wubq6H5E2VXGjj8sZrqd3dH53TXVuVmcXF1d2zbjebiN1j5VQ6tV/okwIWY+4UnX+CE/EfB83ojzLBPIGUHo5pydJHnc0Bev1D1UMsc6jbMFm/EgXfEuN5Ujw3HvE7EAMBPABANVEWOdJG98fT5U2muFxcbenDiltXYV+13ry1T3e92jIHJPW8lMvlWq3WbF5e9kfOsfPk9Kn9/cumNJDv7kaa2i+EGFeCE/Afzx1PVOkaZTdfgF8RwvaSvDZAr3+oeohlvrPtzUj0RzJHxd4S43pSXBE88TsQBwE8AEARZhybYO0wv0Mi94mD3oGNpdBcaKwl4n4OjeCtL1tXa31vhEDGKuKdTqdzenpar694VyVLa2Glbn9H3x7My5cU80iQ4MyqWy7CTRCy7kFWHKPsRohuzIZ2vAZvBL75pid3ZvxuZSTNI5mZJLdE5nfpGJ8URwRP/A7EQgAPAFBFrMAz0iHWys9hzP61z4lalaiD2kXmQmMteheeBGsBfCOqsy+Jfyrp1FsRUmI+pbmwsKIH88ZIYKSoIUmCs6tuudCbwBonzuctfIN9KXpjATsrirIN0BsJCl6xbdJdbRnrsNkX8k/wSGYvwS2R9V061ifFWJPv9z9uid+BeAjgAQCqsAWeq1vSFcnN7qIxudp2yPfe+dZXP+5GjnztO5v5nyhCuFVoLrxJqMjTYKTkH7uxBoJvrQgpBSsokGQ3W34Jzqy65YJr1D4SmndMY82T/0UPo+w1bQ7Q//izNslD5fj9tlUx69qx51mCRzJ7SW6JnO9SZwKyflL0DHff/fLLdCyuAIxP6Ms+AABEZ6x65r+IXSBjaNXvbVT7S5jl2mXf+la/f2lNrrYfbjvEdkT/0rl5uuOC8ky4TmR+2XZdZ6bNA9y5GU8uAvUdB2rbAthLwLWCnDwp9mIYedecc6bGtzRkpS3PrqeEnOXtd+8lS3B21R3hdmqaF3CmSn5b+lRz2Oc+nLeCX5lKS9Y/dxFKI7NchJ7RfTdLvpjmkZT+1CX5NP4tEfsuDf6BHdOTYn5Dq7iytnFlvLsWeMwI4AEAWco5gB9FWUcpKF52f7Mpu2B4JBjpuq7gx366ceQiRD/6ilTxisEnB76lERrfRkyW/72XLMGZVXfC28nz/ZwCeGc6Am872UPtk7vw0sg+gI/G57yxH8nsA/j4t0T4IT73dPJCCDprtG9b7DVH/A5ExhR6AIBSVk5Hl9LdxXTlZt+9UPTKaV96SLn5n/pn8a4tP5HPda1JsTprgekic2GkbuV01Jdv1GY/e63ZH506p02vnPqnvVxrmrMCnG+oB5SGzMppYFxWrjX7p1FncydLcIbV7Zcs//OLcu0yegbTMd50F0Iyjdn+YZz587FLYyzKtUtPvekSPJLZS3BLxLxLw64/jifFZLtJmD8PREcADwBQzcppR5vVWbZP8tamgcu3eVpYOe1oR1jf9u3JB1lYOe30+5c115Uv5dddqHecIbK921tgLmwJPO3o0+UdyRDlcrnWbF72R51T2endCTFTPuqc1lekW8+FlIY0cWbaHCmrNS/7fXm6fCVKcKbVLaWf35EwM13ZL+jvxxake5c6sLYhiPf+e/zSyE25XK7pz1VgqcZ/JLOX4JaId5eGGMuTYh1mrHZB/A7EMDMajYpOAwAAAIBH5WprZvVMjGl6AzA1GIEHAAAAMFbmyvqMvwOxEMADAAAAGJ/bK3OPSseefgBCfVJ0AgAAAAA8AsaseVO5+e14FmsEpgYj8AAAAADyZy2KKITQNtFg+B2IhwAeAAAAQP4WPvuLuYtGyk00gMeKVegBAAAAAFAAI/AAAAAAACiAAB4AAAAAAAUQwAMAAAAAoAACeAAAAAAAFEAADwAAAACAAgjgAQAAAABQAAE8AAAAAAAKIIAHAAAAAEABBPAAAAAAACiAAB4AAAAAAAUQwAMAAAAAoAACeAAAAAAAFEAADwAAAACAAgjgAQAAAABQwP8BJ6p2ddt/r70AAAAASUVORK5CYII=" width="672" /></p>
<div class="sourceCode" id="cb127"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb127-1"><a href="#cb127-1" tabindex="-1"></a> <span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_1.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">7</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
</div>
<div id="hypothesis-2-figures-gender" class="section level3">
<h3>Hypothesis 2 figures-Gender</h3>
<div class="sourceCode" id="cb128"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb128-1"><a href="#cb128-1" tabindex="-1"></a><span class="co">#bring in the loaded models </span></span>
<span id="cb128-2"><a href="#cb128-2" tabindex="-1"></a>Americas_hyp2 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Americas_hyp2.rds&quot;</span>)</span>
<span id="cb128-3"><a href="#cb128-3" tabindex="-1"></a>CSES_hyp2 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;CSES_hyp2.rds&quot;</span>)</span>
<span id="cb128-4"><a href="#cb128-4" tabindex="-1"></a>Euro_hyp2 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Euro_hyp2.rds&quot;</span>)</span>
<span id="cb128-5"><a href="#cb128-5" tabindex="-1"></a>Latino_hyp2 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Latino_hyp2.rds&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb129"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb129-1"><a href="#cb129-1" tabindex="-1"></a><span class="fu">library</span>(sjPlot)</span>
<span id="cb129-2"><a href="#cb129-2" tabindex="-1"></a><span class="fu">library</span>(dplyr)</span>
<span id="cb129-3"><a href="#cb129-3" tabindex="-1"></a>Americas_hyp2_fig <span class="ot">&lt;-</span> sjPlot<span class="sc">::</span><span class="fu">plot_model</span>(Americas_hyp2,</span>
<span id="cb129-4"><a href="#cb129-4" tabindex="-1"></a>                                 <span class="at">type =</span> <span class="st">&quot;pred&quot;</span>, </span>
<span id="cb129-5"><a href="#cb129-5" tabindex="-1"></a>                                 <span class="at">terms =</span> <span class="fu">c</span>(<span class="st">&quot;quota_effect&quot;</span>, <span class="st">&quot;Female&quot;</span>))</span>
<span id="cb129-6"><a href="#cb129-6" tabindex="-1"></a>  </span>
<span id="cb129-7"><a href="#cb129-7" tabindex="-1"></a>Americas_hyp2_fig_data <span class="ot">&lt;-</span> Americas_hyp2_fig<span class="sc">$</span>data</span>
<span id="cb129-8"><a href="#cb129-8" tabindex="-1"></a></span>
<span id="cb129-9"><a href="#cb129-9" tabindex="-1"></a></span>
<span id="cb129-10"><a href="#cb129-10" tabindex="-1"></a>Americas_hyp2_fig_data <span class="ot">&lt;-</span> Americas_hyp2_fig_data <span class="sc">%&gt;%</span></span>
<span id="cb129-11"><a href="#cb129-11" tabindex="-1"></a>  <span class="fu">mutate</span>( <span class="at">Quota =</span> <span class="fu">case_when</span>(</span>
<span id="cb129-12"><a href="#cb129-12" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;No quota&quot;</span>,</span>
<span id="cb129-13"><a href="#cb129-13" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Non-effective quota&quot;</span>,</span>
<span id="cb129-14"><a href="#cb129-14" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Effective quota&quot;</span>),</span>
<span id="cb129-15"><a href="#cb129-15" tabindex="-1"></a>          <span class="at">group =</span> <span class="fu">as.factor</span>(group),</span>
<span id="cb129-16"><a href="#cb129-16" tabindex="-1"></a>            <span class="at">Upper =</span> predicted <span class="sc">+</span> <span class="fl">1.41</span><span class="sc">*</span>std.error,</span>
<span id="cb129-17"><a href="#cb129-17" tabindex="-1"></a>            <span class="at">Lower =</span> predicted <span class="sc">-</span> <span class="fl">1.41</span><span class="sc">*</span>std.error, </span>
<span id="cb129-18"><a href="#cb129-18" tabindex="-1"></a>            <span class="at">Data =</span> <span class="st">&quot;AmericasBarometer&quot;</span>)</span>
<span id="cb129-19"><a href="#cb129-19" tabindex="-1"></a></span>
<span id="cb129-20"><a href="#cb129-20" tabindex="-1"></a></span>
<span id="cb129-21"><a href="#cb129-21" tabindex="-1"></a>CSES_hyp2_fig <span class="ot">&lt;-</span> sjPlot<span class="sc">::</span><span class="fu">plot_model</span>(CSES_hyp2,</span>
<span id="cb129-22"><a href="#cb129-22" tabindex="-1"></a>                                 <span class="at">type =</span> <span class="st">&quot;pred&quot;</span>, </span>
<span id="cb129-23"><a href="#cb129-23" tabindex="-1"></a>                                 <span class="at">terms =</span> <span class="fu">c</span>(<span class="st">&quot;quota_effect&quot;</span>, <span class="st">&quot;Female&quot;</span>))</span>
<span id="cb129-24"><a href="#cb129-24" tabindex="-1"></a></span>
<span id="cb129-25"><a href="#cb129-25" tabindex="-1"></a>CSES_hyp2_fig_data <span class="ot">&lt;-</span> CSES_hyp2_fig<span class="sc">$</span>data</span>
<span id="cb129-26"><a href="#cb129-26" tabindex="-1"></a></span>
<span id="cb129-27"><a href="#cb129-27" tabindex="-1"></a></span>
<span id="cb129-28"><a href="#cb129-28" tabindex="-1"></a>CSES_hyp2_fig_data <span class="ot">&lt;-</span> CSES_hyp2_fig_data <span class="sc">%&gt;%</span></span>
<span id="cb129-29"><a href="#cb129-29" tabindex="-1"></a>  <span class="fu">mutate</span>( <span class="at">Quota =</span> <span class="fu">case_when</span>(</span>
<span id="cb129-30"><a href="#cb129-30" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;No quota&quot;</span>,</span>
<span id="cb129-31"><a href="#cb129-31" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Non-effective quota&quot;</span>,</span>
<span id="cb129-32"><a href="#cb129-32" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Effective quota&quot;</span>),</span>
<span id="cb129-33"><a href="#cb129-33" tabindex="-1"></a>          <span class="at">group =</span> <span class="fu">as.factor</span>(group),</span>
<span id="cb129-34"><a href="#cb129-34" tabindex="-1"></a>            <span class="at">Upper =</span> predicted <span class="sc">+</span> <span class="fl">1.41</span><span class="sc">*</span>std.error,</span>
<span id="cb129-35"><a href="#cb129-35" tabindex="-1"></a>            <span class="at">Lower =</span> predicted <span class="sc">-</span> <span class="fl">1.41</span><span class="sc">*</span>std.error, </span>
<span id="cb129-36"><a href="#cb129-36" tabindex="-1"></a>            <span class="at">Data =</span> <span class="st">&quot;CSES&quot;</span>)</span>
<span id="cb129-37"><a href="#cb129-37" tabindex="-1"></a></span>
<span id="cb129-38"><a href="#cb129-38" tabindex="-1"></a></span>
<span id="cb129-39"><a href="#cb129-39" tabindex="-1"></a></span>
<span id="cb129-40"><a href="#cb129-40" tabindex="-1"></a>Euro_hyp2_fig <span class="ot">&lt;-</span> sjPlot<span class="sc">::</span><span class="fu">plot_model</span>(Euro_hyp2,</span>
<span id="cb129-41"><a href="#cb129-41" tabindex="-1"></a>                                 <span class="at">type =</span> <span class="st">&quot;pred&quot;</span>, </span>
<span id="cb129-42"><a href="#cb129-42" tabindex="-1"></a>                                 <span class="at">terms =</span> <span class="fu">c</span>(<span class="st">&quot;quota_effect&quot;</span>, <span class="st">&quot;Female&quot;</span>))</span>
<span id="cb129-43"><a href="#cb129-43" tabindex="-1"></a></span>
<span id="cb129-44"><a href="#cb129-44" tabindex="-1"></a>Euro_hyp2_fig_data <span class="ot">&lt;-</span> Euro_hyp2_fig<span class="sc">$</span>data</span>
<span id="cb129-45"><a href="#cb129-45" tabindex="-1"></a></span>
<span id="cb129-46"><a href="#cb129-46" tabindex="-1"></a></span>
<span id="cb129-47"><a href="#cb129-47" tabindex="-1"></a>Euro_hyp2_fig_data <span class="ot">&lt;-</span> Euro_hyp2_fig_data <span class="sc">%&gt;%</span></span>
<span id="cb129-48"><a href="#cb129-48" tabindex="-1"></a>  <span class="fu">mutate</span>( <span class="at">Quota =</span> <span class="fu">case_when</span>(</span>
<span id="cb129-49"><a href="#cb129-49" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;No quota&quot;</span>,</span>
<span id="cb129-50"><a href="#cb129-50" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Non-effective quota&quot;</span>,</span>
<span id="cb129-51"><a href="#cb129-51" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Effective quota&quot;</span>),</span>
<span id="cb129-52"><a href="#cb129-52" tabindex="-1"></a>          <span class="at">group =</span> <span class="fu">as.factor</span>(group),</span>
<span id="cb129-53"><a href="#cb129-53" tabindex="-1"></a>            <span class="at">Upper =</span> predicted <span class="sc">+</span> <span class="fl">1.41</span><span class="sc">*</span>std.error,</span>
<span id="cb129-54"><a href="#cb129-54" tabindex="-1"></a>            <span class="at">Lower =</span> predicted <span class="sc">-</span> <span class="fl">1.41</span><span class="sc">*</span>std.error, </span>
<span id="cb129-55"><a href="#cb129-55" tabindex="-1"></a>            <span class="at">Data =</span> <span class="st">&quot;Eurobarometer&quot;</span>)</span>
<span id="cb129-56"><a href="#cb129-56" tabindex="-1"></a></span>
<span id="cb129-57"><a href="#cb129-57" tabindex="-1"></a></span>
<span id="cb129-58"><a href="#cb129-58" tabindex="-1"></a></span>
<span id="cb129-59"><a href="#cb129-59" tabindex="-1"></a>Latino_hyp2_fig <span class="ot">&lt;-</span> sjPlot<span class="sc">::</span><span class="fu">plot_model</span>(Latino_hyp2,</span>
<span id="cb129-60"><a href="#cb129-60" tabindex="-1"></a>                                 <span class="at">type =</span> <span class="st">&quot;pred&quot;</span>, </span>
<span id="cb129-61"><a href="#cb129-61" tabindex="-1"></a>                                 <span class="at">terms =</span> <span class="fu">c</span>(<span class="st">&quot;quota_effect&quot;</span>, <span class="st">&quot;Female&quot;</span>))</span>
<span id="cb129-62"><a href="#cb129-62" tabindex="-1"></a></span>
<span id="cb129-63"><a href="#cb129-63" tabindex="-1"></a>Latino_hyp2_fig_data <span class="ot">&lt;-</span> Latino_hyp2_fig<span class="sc">$</span>data</span>
<span id="cb129-64"><a href="#cb129-64" tabindex="-1"></a></span>
<span id="cb129-65"><a href="#cb129-65" tabindex="-1"></a></span>
<span id="cb129-66"><a href="#cb129-66" tabindex="-1"></a>Latino_hyp2_fig_data <span class="ot">&lt;-</span> Latino_hyp2_fig_data <span class="sc">%&gt;%</span></span>
<span id="cb129-67"><a href="#cb129-67" tabindex="-1"></a>  <span class="fu">mutate</span>( <span class="at">Quota =</span> <span class="fu">case_when</span>(</span>
<span id="cb129-68"><a href="#cb129-68" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;No quota&quot;</span>,</span>
<span id="cb129-69"><a href="#cb129-69" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Non-effective quota&quot;</span>,</span>
<span id="cb129-70"><a href="#cb129-70" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Effective quota&quot;</span>),</span>
<span id="cb129-71"><a href="#cb129-71" tabindex="-1"></a>          <span class="at">group =</span> <span class="fu">as.factor</span>(group),</span>
<span id="cb129-72"><a href="#cb129-72" tabindex="-1"></a>            <span class="at">Upper =</span> predicted <span class="sc">+</span> <span class="fl">1.41</span><span class="sc">*</span>std.error,</span>
<span id="cb129-73"><a href="#cb129-73" tabindex="-1"></a>            <span class="at">Lower =</span> predicted <span class="sc">-</span> <span class="fl">1.41</span><span class="sc">*</span>std.error, </span>
<span id="cb129-74"><a href="#cb129-74" tabindex="-1"></a>            <span class="at">Data =</span> <span class="st">&quot;Latinobarometer&quot;</span>)</span>
<span id="cb129-75"><a href="#cb129-75" tabindex="-1"></a></span>
<span id="cb129-76"><a href="#cb129-76" tabindex="-1"></a></span>
<span id="cb129-77"><a href="#cb129-77" tabindex="-1"></a>hyp2_fig_data <span class="ot">&lt;-</span> <span class="fu">rbind</span>(Americas_hyp2_fig_data,</span>
<span id="cb129-78"><a href="#cb129-78" tabindex="-1"></a>      CSES_hyp2_fig_data,</span>
<span id="cb129-79"><a href="#cb129-79" tabindex="-1"></a>      Euro_hyp2_fig_data,</span>
<span id="cb129-80"><a href="#cb129-80" tabindex="-1"></a>      Latino_hyp2_fig_data)</span>
<span id="cb129-81"><a href="#cb129-81" tabindex="-1"></a></span>
<span id="cb129-82"><a href="#cb129-82" tabindex="-1"></a></span>
<span id="cb129-83"><a href="#cb129-83" tabindex="-1"></a>hyp2_fig_data<span class="sc">$</span>Quota <span class="ot">&lt;-</span> <span class="fu">as.factor</span>(hyp2_fig_data<span class="sc">$</span>Quota)</span>
<span id="cb129-84"><a href="#cb129-84" tabindex="-1"></a></span>
<span id="cb129-85"><a href="#cb129-85" tabindex="-1"></a>hyp2_fig_data<span class="sc">$</span>Quota <span class="ot">&lt;-</span> <span class="fu">factor</span>(hyp2_fig_data<span class="sc">$</span>Quota , <span class="at">levels =</span> <span class="fu">c</span>(<span class="st">&quot;No quota&quot;</span>, <span class="st">&quot;Non-effective quota&quot;</span>, <span class="st">&quot;Effective quota&quot;</span>))</span>
<span id="cb129-86"><a href="#cb129-86" tabindex="-1"></a></span>
<span id="cb129-87"><a href="#cb129-87" tabindex="-1"></a>hyp2_fig_data <span class="ot">&lt;-</span> <span class="fu">na.omit</span>(hyp2_fig_data)</span></code></pre></div>
<div class="sourceCode" id="cb130"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb130-1"><a href="#cb130-1" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb130-2"><a href="#cb130-2" tabindex="-1"></a><span class="fu">ggplot</span>(hyp2_fig_data, <span class="fu">aes</span>(<span class="at">x=</span>Quota, <span class="at">y=</span>predicted, <span class="at">fill =</span> group)) <span class="sc">+</span> </span>
<span id="cb130-3"><a href="#cb130-3" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">stat =</span> <span class="st">&quot;identity&quot;</span>,<span class="at">position =</span> <span class="st">&quot;dodge&quot;</span>, <span class="at">width=</span>.<span class="dv">9</span>) <span class="sc">+</span> </span>
<span id="cb130-4"><a href="#cb130-4" tabindex="-1"></a>      <span class="fu">scale_x_discrete</span>(<span class="at">limits=</span>rev) <span class="sc">+</span></span>
<span id="cb130-5"><a href="#cb130-5" tabindex="-1"></a></span>
<span id="cb130-6"><a href="#cb130-6" tabindex="-1"></a>  <span class="fu">ylim</span>(<span class="dv">0</span>,<span class="fl">0.65</span>) <span class="sc">+</span></span>
<span id="cb130-7"><a href="#cb130-7" tabindex="-1"></a>       <span class="fu">geom_text</span>(<span class="fu">aes</span>(<span class="at">label=</span><span class="fu">sprintf</span>(<span class="st">&quot;%0.3f&quot;</span>, predicted)), <span class="at">position=</span><span class="fu">position_dodge</span>(<span class="at">width=</span><span class="fl">0.9</span>), <span class="at">hjust =</span> <span class="fl">2.5</span>, <span class="at">vjust=</span><span class="dv">0</span>, </span>
<span id="cb130-8"><a href="#cb130-8" tabindex="-1"></a>                 <span class="at">size=</span><span class="fl">3.8</span>,</span>
<span id="cb130-9"><a href="#cb130-9" tabindex="-1"></a>                 <span class="at">colour =</span> <span class="st">&quot;white&quot;</span>, <span class="at">fontface =</span> <span class="st">&quot;bold&quot;</span>) <span class="sc">+</span> </span>
<span id="cb130-10"><a href="#cb130-10" tabindex="-1"></a>  </span>
<span id="cb130-11"><a href="#cb130-11" tabindex="-1"></a>   <span class="fu">geom_text</span>(<span class="fu">aes</span>(<span class="at">label=</span><span class="fu">sprintf</span>(<span class="st">&quot;(%0.3f, %0.3f)&quot;</span>, Lower, Upper)), <span class="at">position=</span><span class="fu">position_dodge</span>(<span class="at">width=</span><span class="fl">0.9</span>), <span class="at">hjust =</span> <span class="fl">1.6</span>, <span class="at">vjust =</span> <span class="dv">2</span>, </span>
<span id="cb130-12"><a href="#cb130-12" tabindex="-1"></a>                 <span class="at">size=</span><span class="fl">2.8</span>, </span>
<span id="cb130-13"><a href="#cb130-13" tabindex="-1"></a>               <span class="at">colour =</span> <span class="st">&quot;white&quot;</span>, <span class="at">fontface =</span> <span class="st">&quot;bold&quot;</span>) <span class="sc">+</span> </span>
<span id="cb130-14"><a href="#cb130-14" tabindex="-1"></a></span>
<span id="cb130-15"><a href="#cb130-15" tabindex="-1"></a>  </span>
<span id="cb130-16"><a href="#cb130-16" tabindex="-1"></a>  <span class="fu">geom_errorbar</span>(<span class="fu">aes</span>(<span class="at">ymin=</span>Lower, <span class="at">ymax=</span>Upper), <span class="at">width=</span><span class="dv">0</span>,</span>
<span id="cb130-17"><a href="#cb130-17" tabindex="-1"></a>                 <span class="at">position=</span><span class="fu">position_dodge</span>(.<span class="dv">9</span>)) <span class="sc">+</span> </span>
<span id="cb130-18"><a href="#cb130-18" tabindex="-1"></a>    <span class="fu">scale_fill_grey</span>(<span class="at">start =</span> <span class="fl">0.75</span>, <span class="at">end =</span> .<span class="dv">35</span>,</span>
<span id="cb130-19"><a href="#cb130-19" tabindex="-1"></a>                   <span class="at">guide =</span> <span class="fu">guide_legend</span>(<span class="at">reverse =</span> <span class="cn">FALSE</span>))  <span class="sc">+</span></span>
<span id="cb130-20"><a href="#cb130-20" tabindex="-1"></a></span>
<span id="cb130-21"><a href="#cb130-21" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">y =</span> <span class="st">&quot;Predicted Satisfaction with Democracy&quot;</span>, </span>
<span id="cb130-22"><a href="#cb130-22" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Quota type&quot;</span>,</span>
<span id="cb130-23"><a href="#cb130-23" tabindex="-1"></a>       <span class="at">title =</span> <span class="st">&quot;&quot;</span>) <span class="sc">+</span></span>
<span id="cb130-24"><a href="#cb130-24" tabindex="-1"></a>    <span class="fu">theme_bw</span>(<span class="dv">12</span>) <span class="sc">+</span></span>
<span id="cb130-25"><a href="#cb130-25" tabindex="-1"></a>    <span class="fu">facet_wrap</span>(<span class="sc">~</span>Data) <span class="sc">+</span> </span>
<span id="cb130-26"><a href="#cb130-26" tabindex="-1"></a></span>
<span id="cb130-27"><a href="#cb130-27" tabindex="-1"></a></span>
<span id="cb130-28"><a href="#cb130-28" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position=</span><span class="st">&quot;bottom&quot;</span>, <span class="at">legend.title =</span> <span class="fu">element_blank</span>()) <span class="sc">+</span> </span>
<span id="cb130-29"><a href="#cb130-29" tabindex="-1"></a>  <span class="fu">coord_flip</span>()</span></code></pre></div>
<pre><code>## Warning in geom_col(stat = &quot;identity&quot;, position = &quot;dodge&quot;, width = 0.9):
## Ignoring unknown parameters: `stat`</code></pre>
<p><img 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" width="672" /></p>
<div class="sourceCode" id="cb132"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb132-1"><a href="#cb132-1" tabindex="-1"></a> <span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_2.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">9</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
</div>
<div id="hypothesis-3a-figures-ideology" class="section level3">
<h3>Hypothesis 3a figures-Ideology</h3>
<div class="sourceCode" id="cb133"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb133-1"><a href="#cb133-1" tabindex="-1"></a><span class="co">#bring in the loaded models </span></span>
<span id="cb133-2"><a href="#cb133-2" tabindex="-1"></a>Americas_hyp3a <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Americas_hyp3a.rds&quot;</span>)</span>
<span id="cb133-3"><a href="#cb133-3" tabindex="-1"></a>CSES_hyp3a <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;CSES_hyp3a.rds&quot;</span>)</span>
<span id="cb133-4"><a href="#cb133-4" tabindex="-1"></a>Euro_hyp3a <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Euro_hyp3a.rds&quot;</span>)</span>
<span id="cb133-5"><a href="#cb133-5" tabindex="-1"></a>Latino_hyp3a <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Latino_hyp3a.rds&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb134"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb134-1"><a href="#cb134-1" tabindex="-1"></a>Americas_hyp3a_fig <span class="ot">&lt;-</span> sjPlot<span class="sc">::</span><span class="fu">plot_model</span>(Americas_hyp3a,</span>
<span id="cb134-2"><a href="#cb134-2" tabindex="-1"></a>                                         <span class="at">type =</span> <span class="st">&quot;int&quot;</span>, <span class="at">mdrt.values =</span> <span class="st">&quot;meansd&quot;</span>)</span>
<span id="cb134-3"><a href="#cb134-3" tabindex="-1"></a></span>
<span id="cb134-4"><a href="#cb134-4" tabindex="-1"></a>Americas_hyp3a_fig_data <span class="ot">&lt;-</span> Americas_hyp3a_fig<span class="sc">$</span>data</span>
<span id="cb134-5"><a href="#cb134-5" tabindex="-1"></a></span>
<span id="cb134-6"><a href="#cb134-6" tabindex="-1"></a></span>
<span id="cb134-7"><a href="#cb134-7" tabindex="-1"></a>Americas_hyp3a_fig_data <span class="ot">&lt;-</span> Americas_hyp3a_fig_data <span class="sc">%&gt;%</span></span>
<span id="cb134-8"><a href="#cb134-8" tabindex="-1"></a>  <span class="fu">mutate</span>( <span class="at">Quota =</span> <span class="fu">case_when</span>(</span>
<span id="cb134-9"><a href="#cb134-9" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;No quota&quot;</span>,</span>
<span id="cb134-10"><a href="#cb134-10" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Non-effective quota&quot;</span>,</span>
<span id="cb134-11"><a href="#cb134-11" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Effective quota&quot;</span>),</span>
<span id="cb134-12"><a href="#cb134-12" tabindex="-1"></a>          <span class="at">Ideology =</span> <span class="fu">case_when</span>(</span>
<span id="cb134-13"><a href="#cb134-13" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="fl">0.23</span> <span class="sc">~</span> <span class="st">&quot;Mean - 1sd (Left)&quot;</span>,</span>
<span id="cb134-14"><a href="#cb134-14" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="fl">0.52</span> <span class="sc">~</span> <span class="st">&quot;Mean (Center)&quot;</span>,</span>
<span id="cb134-15"><a href="#cb134-15" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="fl">0.8</span> <span class="sc">~</span> <span class="st">&quot;Mean + 1sd (Right)&quot;</span>),</span>
<span id="cb134-16"><a href="#cb134-16" tabindex="-1"></a>          <span class="at">Ideology =</span> <span class="fu">as.factor</span>(Ideology),</span>
<span id="cb134-17"><a href="#cb134-17" tabindex="-1"></a>            <span class="at">Upper =</span> predicted <span class="sc">+</span> <span class="fl">1.41</span><span class="sc">*</span>std.error,</span>
<span id="cb134-18"><a href="#cb134-18" tabindex="-1"></a>            <span class="at">Lower =</span> predicted <span class="sc">-</span> <span class="fl">1.41</span><span class="sc">*</span>std.error, </span>
<span id="cb134-19"><a href="#cb134-19" tabindex="-1"></a>            <span class="at">Data =</span> <span class="st">&quot;AmericasBarometer&quot;</span>)</span>
<span id="cb134-20"><a href="#cb134-20" tabindex="-1"></a></span>
<span id="cb134-21"><a href="#cb134-21" tabindex="-1"></a></span>
<span id="cb134-22"><a href="#cb134-22" tabindex="-1"></a></span>
<span id="cb134-23"><a href="#cb134-23" tabindex="-1"></a>CSES_hyp3a_fig <span class="ot">&lt;-</span> sjPlot<span class="sc">::</span><span class="fu">plot_model</span>(CSES_hyp3a,</span>
<span id="cb134-24"><a href="#cb134-24" tabindex="-1"></a>                                         <span class="at">type =</span> <span class="st">&quot;int&quot;</span>, <span class="at">mdrt.values =</span> <span class="st">&quot;meansd&quot;</span>)</span>
<span id="cb134-25"><a href="#cb134-25" tabindex="-1"></a></span>
<span id="cb134-26"><a href="#cb134-26" tabindex="-1"></a>CSES_hyp3a_fig_data <span class="ot">&lt;-</span> CSES_hyp3a_fig<span class="sc">$</span>data</span>
<span id="cb134-27"><a href="#cb134-27" tabindex="-1"></a></span>
<span id="cb134-28"><a href="#cb134-28" tabindex="-1"></a></span>
<span id="cb134-29"><a href="#cb134-29" tabindex="-1"></a>CSES_hyp3a_fig_data <span class="ot">&lt;-</span> CSES_hyp3a_fig_data <span class="sc">%&gt;%</span></span>
<span id="cb134-30"><a href="#cb134-30" tabindex="-1"></a>  <span class="fu">mutate</span>( <span class="at">Quota =</span> <span class="fu">case_when</span>(</span>
<span id="cb134-31"><a href="#cb134-31" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;No quota&quot;</span>,</span>
<span id="cb134-32"><a href="#cb134-32" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Non-effective quota&quot;</span>,</span>
<span id="cb134-33"><a href="#cb134-33" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Effective quota&quot;</span>),</span>
<span id="cb134-34"><a href="#cb134-34" tabindex="-1"></a>          <span class="at">Ideology =</span> <span class="fu">case_when</span>(</span>
<span id="cb134-35"><a href="#cb134-35" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="fl">0.23</span> <span class="sc">~</span> <span class="st">&quot;Mean - 1sd (Left)&quot;</span>,</span>
<span id="cb134-36"><a href="#cb134-36" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="fl">0.52</span> <span class="sc">~</span> <span class="st">&quot;Mean (Center)&quot;</span>,</span>
<span id="cb134-37"><a href="#cb134-37" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="fl">0.8</span> <span class="sc">~</span> <span class="st">&quot;Mean + 1sd (Right)&quot;</span>),</span>
<span id="cb134-38"><a href="#cb134-38" tabindex="-1"></a>          <span class="at">Ideology =</span> <span class="fu">as.factor</span>(Ideology),</span>
<span id="cb134-39"><a href="#cb134-39" tabindex="-1"></a>            <span class="at">Upper =</span> predicted <span class="sc">+</span> <span class="fl">1.41</span><span class="sc">*</span>std.error,</span>
<span id="cb134-40"><a href="#cb134-40" tabindex="-1"></a>            <span class="at">Lower =</span> predicted <span class="sc">-</span> <span class="fl">1.41</span><span class="sc">*</span>std.error, </span>
<span id="cb134-41"><a href="#cb134-41" tabindex="-1"></a>            <span class="at">Data =</span> <span class="st">&quot;CSES&quot;</span>)</span>
<span id="cb134-42"><a href="#cb134-42" tabindex="-1"></a></span>
<span id="cb134-43"><a href="#cb134-43" tabindex="-1"></a></span>
<span id="cb134-44"><a href="#cb134-44" tabindex="-1"></a>CSES_hyp3a_fig <span class="ot">&lt;-</span> sjPlot<span class="sc">::</span><span class="fu">plot_model</span>(CSES_hyp3a,</span>
<span id="cb134-45"><a href="#cb134-45" tabindex="-1"></a>                                         <span class="at">type =</span> <span class="st">&quot;int&quot;</span>, <span class="at">mdrt.values =</span> <span class="st">&quot;meansd&quot;</span>)</span>
<span id="cb134-46"><a href="#cb134-46" tabindex="-1"></a></span>
<span id="cb134-47"><a href="#cb134-47" tabindex="-1"></a>CSES_hyp3a_fig_data <span class="ot">&lt;-</span> CSES_hyp3a_fig<span class="sc">$</span>data</span>
<span id="cb134-48"><a href="#cb134-48" tabindex="-1"></a></span>
<span id="cb134-49"><a href="#cb134-49" tabindex="-1"></a></span>
<span id="cb134-50"><a href="#cb134-50" tabindex="-1"></a>CSES_hyp3a_fig_data <span class="ot">&lt;-</span> CSES_hyp3a_fig_data <span class="sc">%&gt;%</span></span>
<span id="cb134-51"><a href="#cb134-51" tabindex="-1"></a>  <span class="fu">mutate</span>( <span class="at">Quota =</span> <span class="fu">case_when</span>(</span>
<span id="cb134-52"><a href="#cb134-52" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;No quota&quot;</span>,</span>
<span id="cb134-53"><a href="#cb134-53" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Non-effective quota&quot;</span>,</span>
<span id="cb134-54"><a href="#cb134-54" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Effective quota&quot;</span>),</span>
<span id="cb134-55"><a href="#cb134-55" tabindex="-1"></a>          <span class="at">Ideology =</span> <span class="fu">case_when</span>(</span>
<span id="cb134-56"><a href="#cb134-56" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="fl">0.29</span> <span class="sc">~</span> <span class="st">&quot;Mean - 1sd (Left)&quot;</span>,</span>
<span id="cb134-57"><a href="#cb134-57" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="fl">0.54</span> <span class="sc">~</span> <span class="st">&quot;Mean (Center)&quot;</span>,</span>
<span id="cb134-58"><a href="#cb134-58" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="fl">0.79</span> <span class="sc">~</span> <span class="st">&quot;Mean + 1sd (Right)&quot;</span>),</span>
<span id="cb134-59"><a href="#cb134-59" tabindex="-1"></a>          <span class="at">Ideology =</span> <span class="fu">as.factor</span>(Ideology),</span>
<span id="cb134-60"><a href="#cb134-60" tabindex="-1"></a>            <span class="at">Upper =</span> predicted <span class="sc">+</span> <span class="fl">1.41</span><span class="sc">*</span>std.error,</span>
<span id="cb134-61"><a href="#cb134-61" tabindex="-1"></a>            <span class="at">Lower =</span> predicted <span class="sc">-</span> <span class="fl">1.41</span><span class="sc">*</span>std.error, </span>
<span id="cb134-62"><a href="#cb134-62" tabindex="-1"></a>            <span class="at">Data =</span> <span class="st">&quot;CSES&quot;</span>)</span>
<span id="cb134-63"><a href="#cb134-63" tabindex="-1"></a></span>
<span id="cb134-64"><a href="#cb134-64" tabindex="-1"></a></span>
<span id="cb134-65"><a href="#cb134-65" tabindex="-1"></a></span>
<span id="cb134-66"><a href="#cb134-66" tabindex="-1"></a>Euro_hyp3a_fig <span class="ot">&lt;-</span> sjPlot<span class="sc">::</span><span class="fu">plot_model</span>(Euro_hyp3a,</span>
<span id="cb134-67"><a href="#cb134-67" tabindex="-1"></a>                                         <span class="at">type =</span> <span class="st">&quot;int&quot;</span>, <span class="at">mdrt.values =</span> <span class="st">&quot;meansd&quot;</span>)</span>
<span id="cb134-68"><a href="#cb134-68" tabindex="-1"></a></span>
<span id="cb134-69"><a href="#cb134-69" tabindex="-1"></a>Euro_hyp3a_fig_data <span class="ot">&lt;-</span> Euro_hyp3a_fig<span class="sc">$</span>data</span>
<span id="cb134-70"><a href="#cb134-70" tabindex="-1"></a></span>
<span id="cb134-71"><a href="#cb134-71" tabindex="-1"></a></span>
<span id="cb134-72"><a href="#cb134-72" tabindex="-1"></a>Euro_hyp3a_fig_data <span class="ot">&lt;-</span> Euro_hyp3a_fig_data <span class="sc">%&gt;%</span></span>
<span id="cb134-73"><a href="#cb134-73" tabindex="-1"></a>  <span class="fu">mutate</span>( <span class="at">Quota =</span> <span class="fu">case_when</span>(</span>
<span id="cb134-74"><a href="#cb134-74" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;No quota&quot;</span>,</span>
<span id="cb134-75"><a href="#cb134-75" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Non-effective quota&quot;</span>,</span>
<span id="cb134-76"><a href="#cb134-76" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Effective quota&quot;</span>),</span>
<span id="cb134-77"><a href="#cb134-77" tabindex="-1"></a>          <span class="at">Ideology =</span> <span class="fu">case_when</span>(</span>
<span id="cb134-78"><a href="#cb134-78" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="fl">0.25</span> <span class="sc">~</span> <span class="st">&quot;Mean - 1sd (Left)&quot;</span>,</span>
<span id="cb134-79"><a href="#cb134-79" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="fl">0.48</span> <span class="sc">~</span> <span class="st">&quot;Mean (Center)&quot;</span>,</span>
<span id="cb134-80"><a href="#cb134-80" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="fl">0.72</span> <span class="sc">~</span> <span class="st">&quot;Mean + 1sd (Right)&quot;</span>),</span>
<span id="cb134-81"><a href="#cb134-81" tabindex="-1"></a>          <span class="at">Ideology =</span> <span class="fu">as.factor</span>(Ideology),</span>
<span id="cb134-82"><a href="#cb134-82" tabindex="-1"></a>            <span class="at">Upper =</span> predicted <span class="sc">+</span> <span class="fl">1.41</span><span class="sc">*</span>std.error,</span>
<span id="cb134-83"><a href="#cb134-83" tabindex="-1"></a>            <span class="at">Lower =</span> predicted <span class="sc">-</span> <span class="fl">1.41</span><span class="sc">*</span>std.error, </span>
<span id="cb134-84"><a href="#cb134-84" tabindex="-1"></a>            <span class="at">Data =</span> <span class="st">&quot;Eurobarometer&quot;</span>)</span>
<span id="cb134-85"><a href="#cb134-85" tabindex="-1"></a></span>
<span id="cb134-86"><a href="#cb134-86" tabindex="-1"></a></span>
<span id="cb134-87"><a href="#cb134-87" tabindex="-1"></a></span>
<span id="cb134-88"><a href="#cb134-88" tabindex="-1"></a>Latino_hyp3a_fig <span class="ot">&lt;-</span> sjPlot<span class="sc">::</span><span class="fu">plot_model</span>(Latino_hyp3a,</span>
<span id="cb134-89"><a href="#cb134-89" tabindex="-1"></a>                                         <span class="at">type =</span> <span class="st">&quot;int&quot;</span>, <span class="at">mdrt.values =</span> <span class="st">&quot;meansd&quot;</span>)</span>
<span id="cb134-90"><a href="#cb134-90" tabindex="-1"></a></span>
<span id="cb134-91"><a href="#cb134-91" tabindex="-1"></a>Latino_hyp3a_fig_data <span class="ot">&lt;-</span> Latino_hyp3a_fig<span class="sc">$</span>data</span>
<span id="cb134-92"><a href="#cb134-92" tabindex="-1"></a></span>
<span id="cb134-93"><a href="#cb134-93" tabindex="-1"></a></span>
<span id="cb134-94"><a href="#cb134-94" tabindex="-1"></a>Latino_hyp3a_fig_data <span class="ot">&lt;-</span> Latino_hyp3a_fig_data <span class="sc">%&gt;%</span></span>
<span id="cb134-95"><a href="#cb134-95" tabindex="-1"></a>  <span class="fu">mutate</span>( <span class="at">Quota =</span> <span class="fu">case_when</span>(</span>
<span id="cb134-96"><a href="#cb134-96" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;No quota&quot;</span>,</span>
<span id="cb134-97"><a href="#cb134-97" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Non-effective quota&quot;</span>,</span>
<span id="cb134-98"><a href="#cb134-98" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Effective quota&quot;</span>),</span>
<span id="cb134-99"><a href="#cb134-99" tabindex="-1"></a>          <span class="at">Ideology =</span> <span class="fu">case_when</span>(</span>
<span id="cb134-100"><a href="#cb134-100" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="fl">0.26</span> <span class="sc">~</span> <span class="st">&quot;Mean - 1sd (Left)&quot;</span>,</span>
<span id="cb134-101"><a href="#cb134-101" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="fl">0.53</span> <span class="sc">~</span> <span class="st">&quot;Mean (Center)&quot;</span>,</span>
<span id="cb134-102"><a href="#cb134-102" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="fl">0.8</span> <span class="sc">~</span> <span class="st">&quot;Mean + 1sd (Right)&quot;</span>),</span>
<span id="cb134-103"><a href="#cb134-103" tabindex="-1"></a>          <span class="at">Ideology =</span> <span class="fu">as.factor</span>(Ideology),</span>
<span id="cb134-104"><a href="#cb134-104" tabindex="-1"></a>            <span class="at">Upper =</span> predicted <span class="sc">+</span> <span class="fl">1.41</span><span class="sc">*</span>std.error,</span>
<span id="cb134-105"><a href="#cb134-105" tabindex="-1"></a>            <span class="at">Lower =</span> predicted <span class="sc">-</span> <span class="fl">1.41</span><span class="sc">*</span>std.error, </span>
<span id="cb134-106"><a href="#cb134-106" tabindex="-1"></a>            <span class="at">Data =</span> <span class="st">&quot;Latinobarometer&quot;</span>)</span>
<span id="cb134-107"><a href="#cb134-107" tabindex="-1"></a></span>
<span id="cb134-108"><a href="#cb134-108" tabindex="-1"></a></span>
<span id="cb134-109"><a href="#cb134-109" tabindex="-1"></a>hyp3a_fig_data <span class="ot">&lt;-</span> <span class="fu">rbind</span>(Americas_hyp3a_fig_data,</span>
<span id="cb134-110"><a href="#cb134-110" tabindex="-1"></a>      CSES_hyp3a_fig_data,</span>
<span id="cb134-111"><a href="#cb134-111" tabindex="-1"></a>      Euro_hyp3a_fig_data,</span>
<span id="cb134-112"><a href="#cb134-112" tabindex="-1"></a>      Latino_hyp3a_fig_data)</span>
<span id="cb134-113"><a href="#cb134-113" tabindex="-1"></a></span>
<span id="cb134-114"><a href="#cb134-114" tabindex="-1"></a></span>
<span id="cb134-115"><a href="#cb134-115" tabindex="-1"></a>hyp3a_fig_data<span class="sc">$</span>Quota <span class="ot">&lt;-</span> <span class="fu">as.factor</span>(hyp3a_fig_data<span class="sc">$</span>Quota)</span>
<span id="cb134-116"><a href="#cb134-116" tabindex="-1"></a></span>
<span id="cb134-117"><a href="#cb134-117" tabindex="-1"></a>hyp3a_fig_data<span class="sc">$</span>Quota <span class="ot">&lt;-</span> <span class="fu">factor</span>(hyp3a_fig_data<span class="sc">$</span>Quota , <span class="at">levels =</span> <span class="fu">c</span>(<span class="st">&quot;No quota&quot;</span>, <span class="st">&quot;Non-effective quota&quot;</span>, <span class="st">&quot;Effective quota&quot;</span>))</span>
<span id="cb134-118"><a href="#cb134-118" tabindex="-1"></a></span>
<span id="cb134-119"><a href="#cb134-119" tabindex="-1"></a></span>
<span id="cb134-120"><a href="#cb134-120" tabindex="-1"></a></span>
<span id="cb134-121"><a href="#cb134-121" tabindex="-1"></a>hyp3a_fig_data<span class="sc">$</span>Ideology <span class="ot">&lt;-</span> <span class="fu">as.factor</span>(hyp3a_fig_data<span class="sc">$</span>Ideology)</span>
<span id="cb134-122"><a href="#cb134-122" tabindex="-1"></a></span>
<span id="cb134-123"><a href="#cb134-123" tabindex="-1"></a>hyp3a_fig_data<span class="sc">$</span>Ideology <span class="ot">&lt;-</span> <span class="fu">factor</span>(hyp3a_fig_data<span class="sc">$</span>Ideology , <span class="at">levels =</span> <span class="fu">c</span>(<span class="st">&quot;Mean - 1sd (Left)&quot;</span>, <span class="st">&quot;Mean (Center)&quot;</span>, <span class="st">&quot;Mean + 1sd (Right)&quot;</span>))</span>
<span id="cb134-124"><a href="#cb134-124" tabindex="-1"></a></span>
<span id="cb134-125"><a href="#cb134-125" tabindex="-1"></a>hyp3a_fig_data <span class="ot">&lt;-</span> <span class="fu">na.omit</span>(hyp3a_fig_data)</span></code></pre></div>
<div class="sourceCode" id="cb135"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb135-1"><a href="#cb135-1" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb135-2"><a href="#cb135-2" tabindex="-1"></a><span class="fu">ggplot</span>(hyp3a_fig_data, <span class="fu">aes</span>(<span class="at">x=</span>Quota, <span class="at">y=</span>predicted, <span class="at">fill =</span> Ideology)) <span class="sc">+</span> </span>
<span id="cb135-3"><a href="#cb135-3" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">stat =</span> <span class="st">&quot;identity&quot;</span>,<span class="at">position =</span> <span class="st">&quot;dodge&quot;</span>, <span class="at">width=</span>.<span class="dv">9</span>) <span class="sc">+</span> </span>
<span id="cb135-4"><a href="#cb135-4" tabindex="-1"></a>      <span class="fu">scale_x_discrete</span>(<span class="at">limits=</span>rev) <span class="sc">+</span></span>
<span id="cb135-5"><a href="#cb135-5" tabindex="-1"></a></span>
<span id="cb135-6"><a href="#cb135-6" tabindex="-1"></a>  <span class="fu">ylim</span>(<span class="dv">0</span>,<span class="fl">0.65</span>) <span class="sc">+</span></span>
<span id="cb135-7"><a href="#cb135-7" tabindex="-1"></a>       <span class="fu">geom_text</span>(<span class="fu">aes</span>(<span class="at">label=</span><span class="fu">sprintf</span>(<span class="st">&quot;%0.3f&quot;</span>, predicted)), <span class="at">position=</span><span class="fu">position_dodge</span>(<span class="at">width=</span><span class="fl">0.9</span>), <span class="at">hjust =</span> <span class="fl">2.5</span>, <span class="at">vjust=</span><span class="dv">0</span>, </span>
<span id="cb135-8"><a href="#cb135-8" tabindex="-1"></a>                 <span class="at">size=</span><span class="fl">3.1</span>,</span>
<span id="cb135-9"><a href="#cb135-9" tabindex="-1"></a>                 <span class="at">colour =</span> <span class="st">&quot;white&quot;</span>, <span class="at">fontface =</span> <span class="st">&quot;bold&quot;</span>) <span class="sc">+</span> </span>
<span id="cb135-10"><a href="#cb135-10" tabindex="-1"></a>  </span>
<span id="cb135-11"><a href="#cb135-11" tabindex="-1"></a>   <span class="fu">geom_text</span>(<span class="fu">aes</span>(<span class="at">label=</span><span class="fu">sprintf</span>(<span class="st">&quot;(%0.3f, %0.3f)&quot;</span>, Lower, Upper)), <span class="at">position=</span><span class="fu">position_dodge</span>(<span class="at">width=</span><span class="fl">0.9</span>), <span class="at">hjust =</span> <span class="fl">1.6</span>, <span class="at">vjust =</span> <span class="dv">2</span>, </span>
<span id="cb135-12"><a href="#cb135-12" tabindex="-1"></a>                 <span class="at">size=</span><span class="fl">2.1</span>, </span>
<span id="cb135-13"><a href="#cb135-13" tabindex="-1"></a>               <span class="at">colour =</span> <span class="st">&quot;white&quot;</span>, <span class="at">fontface =</span> <span class="st">&quot;bold&quot;</span>) <span class="sc">+</span> </span>
<span id="cb135-14"><a href="#cb135-14" tabindex="-1"></a></span>
<span id="cb135-15"><a href="#cb135-15" tabindex="-1"></a>  </span>
<span id="cb135-16"><a href="#cb135-16" tabindex="-1"></a>  <span class="fu">geom_errorbar</span>(<span class="fu">aes</span>(<span class="at">ymin=</span>Lower, <span class="at">ymax=</span>Upper), <span class="at">width=</span><span class="dv">0</span>,</span>
<span id="cb135-17"><a href="#cb135-17" tabindex="-1"></a>                 <span class="at">position=</span><span class="fu">position_dodge</span>(.<span class="dv">9</span>)) <span class="sc">+</span> </span>
<span id="cb135-18"><a href="#cb135-18" tabindex="-1"></a>    <span class="fu">scale_fill_grey</span>(<span class="at">start =</span> <span class="fl">0.75</span>, <span class="at">end =</span> .<span class="dv">35</span>,</span>
<span id="cb135-19"><a href="#cb135-19" tabindex="-1"></a>                   <span class="at">guide =</span> <span class="fu">guide_legend</span>(<span class="at">reverse =</span> <span class="cn">FALSE</span>))  <span class="sc">+</span></span>
<span id="cb135-20"><a href="#cb135-20" tabindex="-1"></a></span>
<span id="cb135-21"><a href="#cb135-21" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">y =</span> <span class="st">&quot;Predicted Satisfaction with Democracy&quot;</span>, </span>
<span id="cb135-22"><a href="#cb135-22" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Quota type&quot;</span>,</span>
<span id="cb135-23"><a href="#cb135-23" tabindex="-1"></a>       <span class="at">title =</span> <span class="st">&quot;&quot;</span>) <span class="sc">+</span></span>
<span id="cb135-24"><a href="#cb135-24" tabindex="-1"></a>    <span class="fu">theme_bw</span>(<span class="dv">12</span>) <span class="sc">+</span></span>
<span id="cb135-25"><a href="#cb135-25" tabindex="-1"></a>    <span class="fu">facet_wrap</span>(<span class="sc">~</span>Data) <span class="sc">+</span> </span>
<span id="cb135-26"><a href="#cb135-26" tabindex="-1"></a></span>
<span id="cb135-27"><a href="#cb135-27" tabindex="-1"></a></span>
<span id="cb135-28"><a href="#cb135-28" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position=</span><span class="st">&quot;bottom&quot;</span>, <span class="at">legend.title =</span> <span class="fu">element_blank</span>()) <span class="sc">+</span> </span>
<span id="cb135-29"><a href="#cb135-29" tabindex="-1"></a>  <span class="fu">coord_flip</span>()</span></code></pre></div>
<pre><code>## Warning in geom_col(stat = &quot;identity&quot;, position = &quot;dodge&quot;, width = 0.9):
## Ignoring unknown parameters: `stat`</code></pre>
<p><img src="data:image/png;base64,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" width="672" /></p>
<div class="sourceCode" id="cb137"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb137-1"><a href="#cb137-1" tabindex="-1"></a> <span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_3.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">11</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
</div>
<div id="hypothesis-3b-figures-quota-support" class="section level3">
<h3>Hypothesis 3b figures-Quota support</h3>
<div class="sourceCode" id="cb138"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb138-1"><a href="#cb138-1" tabindex="-1"></a>Americas_hyp3b <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Americas_hyp3b.rds&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb139"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb139-1"><a href="#cb139-1" tabindex="-1"></a>Americas_hyp3b_fig <span class="ot">&lt;-</span> sjPlot<span class="sc">::</span><span class="fu">plot_model</span>(Americas_hyp3b,</span>
<span id="cb139-2"><a href="#cb139-2" tabindex="-1"></a>                                         <span class="at">type =</span> <span class="st">&quot;int&quot;</span>, <span class="at">mdrt.values =</span> <span class="st">&quot;meansd&quot;</span>)</span>
<span id="cb139-3"><a href="#cb139-3" tabindex="-1"></a></span>
<span id="cb139-4"><a href="#cb139-4" tabindex="-1"></a>Americas_hyp3b_fig_data <span class="ot">&lt;-</span> Americas_hyp3b_fig<span class="sc">$</span>data</span>
<span id="cb139-5"><a href="#cb139-5" tabindex="-1"></a></span>
<span id="cb139-6"><a href="#cb139-6" tabindex="-1"></a></span>
<span id="cb139-7"><a href="#cb139-7" tabindex="-1"></a>Americas_hyp3b_fig_data <span class="ot">&lt;-</span> Americas_hyp3b_fig_data <span class="sc">%&gt;%</span></span>
<span id="cb139-8"><a href="#cb139-8" tabindex="-1"></a>  <span class="fu">mutate</span>( <span class="at">Quota =</span> <span class="fu">case_when</span>(</span>
<span id="cb139-9"><a href="#cb139-9" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;No quota&quot;</span>,</span>
<span id="cb139-10"><a href="#cb139-10" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Non-effective quota&quot;</span>,</span>
<span id="cb139-11"><a href="#cb139-11" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Effective quota&quot;</span>),</span>
<span id="cb139-12"><a href="#cb139-12" tabindex="-1"></a>          <span class="at">Quota_support =</span> <span class="fu">case_when</span>(</span>
<span id="cb139-13"><a href="#cb139-13" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="fl">0.37</span> <span class="sc">~</span> <span class="st">&quot;Mean - 1sd (Do not support quotas)&quot;</span>,</span>
<span id="cb139-14"><a href="#cb139-14" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="fl">0.68</span> <span class="sc">~</span> <span class="st">&quot;Mean&quot;</span>,</span>
<span id="cb139-15"><a href="#cb139-15" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="fl">0.99</span> <span class="sc">~</span> <span class="st">&quot;Mean + 1sd (Support quotas)&quot;</span>),</span>
<span id="cb139-16"><a href="#cb139-16" tabindex="-1"></a>          <span class="at">Quota_support =</span> <span class="fu">as.factor</span>(Quota_support),</span>
<span id="cb139-17"><a href="#cb139-17" tabindex="-1"></a>            <span class="at">Upper =</span> predicted <span class="sc">+</span> <span class="fl">1.41</span><span class="sc">*</span>std.error,</span>
<span id="cb139-18"><a href="#cb139-18" tabindex="-1"></a>            <span class="at">Lower =</span> predicted <span class="sc">-</span> <span class="fl">1.41</span><span class="sc">*</span>std.error, </span>
<span id="cb139-19"><a href="#cb139-19" tabindex="-1"></a>            <span class="at">Data =</span> <span class="st">&quot;AmericasBarometer&quot;</span>)</span>
<span id="cb139-20"><a href="#cb139-20" tabindex="-1"></a></span>
<span id="cb139-21"><a href="#cb139-21" tabindex="-1"></a></span>
<span id="cb139-22"><a href="#cb139-22" tabindex="-1"></a>Americas_hyp3b_fig_data<span class="sc">$</span>Quota <span class="ot">&lt;-</span> <span class="fu">as.factor</span>(Americas_hyp3b_fig_data<span class="sc">$</span>Quota)</span>
<span id="cb139-23"><a href="#cb139-23" tabindex="-1"></a></span>
<span id="cb139-24"><a href="#cb139-24" tabindex="-1"></a>Americas_hyp3b_fig_data<span class="sc">$</span>Quota <span class="ot">&lt;-</span> <span class="fu">factor</span>(Americas_hyp3b_fig_data<span class="sc">$</span>Quota , <span class="at">levels =</span> <span class="fu">c</span>(<span class="st">&quot;No quota&quot;</span>, <span class="st">&quot;Non-effective quota&quot;</span>, <span class="st">&quot;Effective quota&quot;</span>))</span>
<span id="cb139-25"><a href="#cb139-25" tabindex="-1"></a></span>
<span id="cb139-26"><a href="#cb139-26" tabindex="-1"></a></span>
<span id="cb139-27"><a href="#cb139-27" tabindex="-1"></a>Americas_hyp3b_fig_data<span class="sc">$</span>Quota_support <span class="ot">&lt;-</span> <span class="fu">as.factor</span>(Americas_hyp3b_fig_data<span class="sc">$</span>Quota_support)</span>
<span id="cb139-28"><a href="#cb139-28" tabindex="-1"></a></span>
<span id="cb139-29"><a href="#cb139-29" tabindex="-1"></a>Americas_hyp3b_fig_data<span class="sc">$</span>Quota_support <span class="ot">&lt;-</span> <span class="fu">factor</span>(Americas_hyp3b_fig_data<span class="sc">$</span>Quota_support , <span class="at">levels =</span> <span class="fu">c</span>(<span class="st">&quot;Mean - 1sd (Do not support quotas)&quot;</span>, <span class="st">&quot;Mean&quot;</span>, <span class="st">&quot;Mean + 1sd (Support quotas)&quot;</span>))</span>
<span id="cb139-30"><a href="#cb139-30" tabindex="-1"></a></span>
<span id="cb139-31"><a href="#cb139-31" tabindex="-1"></a>Americas_hyp3b_fig_data <span class="ot">&lt;-</span> <span class="fu">na.omit</span>(Americas_hyp3b_fig_data)</span></code></pre></div>
<div class="sourceCode" id="cb140"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb140-1"><a href="#cb140-1" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb140-2"><a href="#cb140-2" tabindex="-1"></a><span class="fu">ggplot</span>(Americas_hyp3b_fig_data, <span class="fu">aes</span>(<span class="at">x=</span>Quota, <span class="at">y=</span>predicted, <span class="at">fill =</span> Quota_support)) <span class="sc">+</span> </span>
<span id="cb140-3"><a href="#cb140-3" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">stat =</span> <span class="st">&quot;identity&quot;</span>,<span class="at">position =</span> <span class="st">&quot;dodge&quot;</span>, <span class="at">width=</span>.<span class="dv">9</span>) <span class="sc">+</span> </span>
<span id="cb140-4"><a href="#cb140-4" tabindex="-1"></a>      <span class="fu">scale_x_discrete</span>(<span class="at">limits=</span>rev) <span class="sc">+</span></span>
<span id="cb140-5"><a href="#cb140-5" tabindex="-1"></a></span>
<span id="cb140-6"><a href="#cb140-6" tabindex="-1"></a>  <span class="fu">ylim</span>(<span class="dv">0</span>,<span class="fl">0.65</span>) <span class="sc">+</span></span>
<span id="cb140-7"><a href="#cb140-7" tabindex="-1"></a>       <span class="fu">geom_text</span>(<span class="fu">aes</span>(<span class="at">label=</span><span class="fu">sprintf</span>(<span class="st">&quot;%0.3f&quot;</span>, predicted)), <span class="at">position=</span><span class="fu">position_dodge</span>(<span class="at">width=</span><span class="fl">0.9</span>), <span class="at">hjust =</span> <span class="fl">3.0</span>, <span class="at">vjust=</span><span class="dv">0</span>, </span>
<span id="cb140-8"><a href="#cb140-8" tabindex="-1"></a>                 <span class="at">size=</span><span class="fl">3.1</span>,</span>
<span id="cb140-9"><a href="#cb140-9" tabindex="-1"></a>                 <span class="at">colour =</span> <span class="st">&quot;white&quot;</span>, <span class="at">fontface =</span> <span class="st">&quot;bold&quot;</span>) <span class="sc">+</span> </span>
<span id="cb140-10"><a href="#cb140-10" tabindex="-1"></a>  </span>
<span id="cb140-11"><a href="#cb140-11" tabindex="-1"></a>   <span class="fu">geom_text</span>(<span class="fu">aes</span>(<span class="at">label=</span><span class="fu">sprintf</span>(<span class="st">&quot;(%0.3f, %0.3f)&quot;</span>, Lower, Upper)), <span class="at">position=</span><span class="fu">position_dodge</span>(<span class="at">width=</span><span class="fl">0.9</span>), <span class="at">hjust =</span> <span class="fl">2.0</span>, <span class="at">vjust =</span> <span class="dv">2</span>, </span>
<span id="cb140-12"><a href="#cb140-12" tabindex="-1"></a>                 <span class="at">size=</span><span class="fl">2.1</span>, </span>
<span id="cb140-13"><a href="#cb140-13" tabindex="-1"></a>               <span class="at">colour =</span> <span class="st">&quot;white&quot;</span>, <span class="at">fontface =</span> <span class="st">&quot;bold&quot;</span>) <span class="sc">+</span> </span>
<span id="cb140-14"><a href="#cb140-14" tabindex="-1"></a></span>
<span id="cb140-15"><a href="#cb140-15" tabindex="-1"></a>  </span>
<span id="cb140-16"><a href="#cb140-16" tabindex="-1"></a>  <span class="fu">geom_errorbar</span>(<span class="fu">aes</span>(<span class="at">ymin=</span>Lower, <span class="at">ymax=</span>Upper), <span class="at">width=</span><span class="dv">0</span>,</span>
<span id="cb140-17"><a href="#cb140-17" tabindex="-1"></a>                 <span class="at">position=</span><span class="fu">position_dodge</span>(.<span class="dv">9</span>)) <span class="sc">+</span> </span>
<span id="cb140-18"><a href="#cb140-18" tabindex="-1"></a>    <span class="fu">scale_fill_grey</span>(<span class="at">start =</span> <span class="fl">0.75</span>, <span class="at">end =</span> .<span class="dv">35</span>,</span>
<span id="cb140-19"><a href="#cb140-19" tabindex="-1"></a>                   <span class="at">guide =</span> <span class="fu">guide_legend</span>(<span class="at">reverse =</span> <span class="cn">FALSE</span>))  <span class="sc">+</span></span>
<span id="cb140-20"><a href="#cb140-20" tabindex="-1"></a></span>
<span id="cb140-21"><a href="#cb140-21" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">y =</span> <span class="st">&quot;Predicted Satisfaction with Democracy&quot;</span>, </span>
<span id="cb140-22"><a href="#cb140-22" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Quota type&quot;</span>,</span>
<span id="cb140-23"><a href="#cb140-23" tabindex="-1"></a>       <span class="at">title =</span> <span class="st">&quot;&quot;</span>) <span class="sc">+</span></span>
<span id="cb140-24"><a href="#cb140-24" tabindex="-1"></a>    <span class="fu">theme_bw</span>(<span class="dv">12</span>) <span class="sc">+</span></span>
<span id="cb140-25"><a href="#cb140-25" tabindex="-1"></a></span>
<span id="cb140-26"><a href="#cb140-26" tabindex="-1"></a></span>
<span id="cb140-27"><a href="#cb140-27" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position=</span><span class="st">&quot;bottom&quot;</span>, <span class="at">legend.title =</span> <span class="fu">element_blank</span>()) <span class="sc">+</span> </span>
<span id="cb140-28"><a href="#cb140-28" tabindex="-1"></a>  <span class="fu">coord_flip</span>()</span></code></pre></div>
<pre><code>## Warning in geom_col(stat = &quot;identity&quot;, position = &quot;dodge&quot;, width = 0.9):
## Ignoring unknown parameters: `stat`</code></pre>
<p><img 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" width="672" /></p>
<div class="sourceCode" id="cb142"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb142-1"><a href="#cb142-1" tabindex="-1"></a> <span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_4.pdf&quot;</span>, <span class="at">width =</span> <span class="fl">7.5</span>, <span class="at">height =</span> <span class="dv">6</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
</div>
<div id="hypothesis-4-figures-corruption" class="section level3">
<h3>Hypothesis 4 figures-Corruption</h3>
<div class="sourceCode" id="cb143"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb143-1"><a href="#cb143-1" tabindex="-1"></a><span class="co">#bring in the loaded models </span></span>
<span id="cb143-2"><a href="#cb143-2" tabindex="-1"></a>Americas_hyp4 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Americas_hyp4.rds&quot;</span>)</span>
<span id="cb143-3"><a href="#cb143-3" tabindex="-1"></a>CSES_hyp4 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;CSES_hyp4.rds&quot;</span>)</span>
<span id="cb143-4"><a href="#cb143-4" tabindex="-1"></a>Euro_hyp4 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Euro_hyp4.rds&quot;</span>)</span>
<span id="cb143-5"><a href="#cb143-5" tabindex="-1"></a>Latino_hyp4 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Latino_hyp4.rds&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb144"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb144-1"><a href="#cb144-1" tabindex="-1"></a>Americas_hyp4_fig <span class="ot">&lt;-</span> sjPlot<span class="sc">::</span><span class="fu">plot_model</span>(Americas_hyp4,</span>
<span id="cb144-2"><a href="#cb144-2" tabindex="-1"></a>                                         <span class="at">type =</span> <span class="st">&quot;int&quot;</span>, <span class="at">mdrt.values =</span> <span class="st">&quot;meansd&quot;</span>)</span>
<span id="cb144-3"><a href="#cb144-3" tabindex="-1"></a></span>
<span id="cb144-4"><a href="#cb144-4" tabindex="-1"></a>Americas_hyp4_fig_data <span class="ot">&lt;-</span> Americas_hyp4_fig<span class="sc">$</span>data</span>
<span id="cb144-5"><a href="#cb144-5" tabindex="-1"></a></span>
<span id="cb144-6"><a href="#cb144-6" tabindex="-1"></a></span>
<span id="cb144-7"><a href="#cb144-7" tabindex="-1"></a>Americas_hyp4_fig_data <span class="ot">&lt;-</span> Americas_hyp4_fig_data <span class="sc">%&gt;%</span></span>
<span id="cb144-8"><a href="#cb144-8" tabindex="-1"></a>  <span class="fu">mutate</span>( <span class="at">Quota =</span> <span class="fu">case_when</span>(</span>
<span id="cb144-9"><a href="#cb144-9" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;No quota&quot;</span>,</span>
<span id="cb144-10"><a href="#cb144-10" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Non-effective quota&quot;</span>,</span>
<span id="cb144-11"><a href="#cb144-11" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Effective quota&quot;</span>),</span>
<span id="cb144-12"><a href="#cb144-12" tabindex="-1"></a>          <span class="at">Corruption =</span> <span class="fu">case_when</span>(</span>
<span id="cb144-13"><a href="#cb144-13" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="fl">0.36</span> <span class="sc">~</span> <span class="st">&quot;Mean - 1sd (Less corrupt)&quot;</span>,</span>
<span id="cb144-14"><a href="#cb144-14" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="fl">0.63</span> <span class="sc">~</span> <span class="st">&quot;Mean&quot;</span>,</span>
<span id="cb144-15"><a href="#cb144-15" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="fl">0.9</span> <span class="sc">~</span> <span class="st">&quot;Mean + 1sd (More corrupt)&quot;</span>),</span>
<span id="cb144-16"><a href="#cb144-16" tabindex="-1"></a>          <span class="at">Corruption =</span> <span class="fu">as.factor</span>(Corruption),</span>
<span id="cb144-17"><a href="#cb144-17" tabindex="-1"></a>            <span class="at">Upper =</span> predicted <span class="sc">+</span> <span class="fl">1.41</span><span class="sc">*</span>std.error,</span>
<span id="cb144-18"><a href="#cb144-18" tabindex="-1"></a>            <span class="at">Lower =</span> predicted <span class="sc">-</span> <span class="fl">1.41</span><span class="sc">*</span>std.error, </span>
<span id="cb144-19"><a href="#cb144-19" tabindex="-1"></a>            <span class="at">Data =</span> <span class="st">&quot;AmericasBarometer&quot;</span>)</span>
<span id="cb144-20"><a href="#cb144-20" tabindex="-1"></a></span>
<span id="cb144-21"><a href="#cb144-21" tabindex="-1"></a></span>
<span id="cb144-22"><a href="#cb144-22" tabindex="-1"></a></span>
<span id="cb144-23"><a href="#cb144-23" tabindex="-1"></a>CSES_hyp4_fig <span class="ot">&lt;-</span> sjPlot<span class="sc">::</span><span class="fu">plot_model</span>(CSES_hyp4,</span>
<span id="cb144-24"><a href="#cb144-24" tabindex="-1"></a>                                         <span class="at">type =</span> <span class="st">&quot;int&quot;</span>, <span class="at">mdrt.values =</span> <span class="st">&quot;meansd&quot;</span>)</span>
<span id="cb144-25"><a href="#cb144-25" tabindex="-1"></a></span>
<span id="cb144-26"><a href="#cb144-26" tabindex="-1"></a>CSES_hyp4_fig_data <span class="ot">&lt;-</span> CSES_hyp4_fig<span class="sc">$</span>data</span>
<span id="cb144-27"><a href="#cb144-27" tabindex="-1"></a></span>
<span id="cb144-28"><a href="#cb144-28" tabindex="-1"></a></span>
<span id="cb144-29"><a href="#cb144-29" tabindex="-1"></a>CSES_hyp4_fig_data <span class="ot">&lt;-</span> CSES_hyp4_fig_data <span class="sc">%&gt;%</span></span>
<span id="cb144-30"><a href="#cb144-30" tabindex="-1"></a>  <span class="fu">mutate</span>( <span class="at">Quota =</span> <span class="fu">case_when</span>(</span>
<span id="cb144-31"><a href="#cb144-31" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;No quota&quot;</span>,</span>
<span id="cb144-32"><a href="#cb144-32" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Non-effective quota&quot;</span>,</span>
<span id="cb144-33"><a href="#cb144-33" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Effective quota&quot;</span>),</span>
<span id="cb144-34"><a href="#cb144-34" tabindex="-1"></a>          <span class="at">Corruption =</span> <span class="fu">case_when</span>(</span>
<span id="cb144-35"><a href="#cb144-35" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="sc">-</span><span class="fl">0.08</span> <span class="sc">~</span> <span class="st">&quot;Mean - 1sd (Less corrupt)&quot;</span>,</span>
<span id="cb144-36"><a href="#cb144-36" tabindex="-1"></a>                           group <span class="sc">==</span>  <span class="fl">0.2</span> <span class="sc">~</span> <span class="st">&quot;Mean&quot;</span>,</span>
<span id="cb144-37"><a href="#cb144-37" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="fl">0.47</span> <span class="sc">~</span> <span class="st">&quot;Mean + 1sd (More corrupt)&quot;</span>),</span>
<span id="cb144-38"><a href="#cb144-38" tabindex="-1"></a>          <span class="at">Corruption =</span> <span class="fu">as.factor</span>(Corruption),</span>
<span id="cb144-39"><a href="#cb144-39" tabindex="-1"></a>            <span class="at">Upper =</span> predicted <span class="sc">+</span> <span class="fl">1.41</span><span class="sc">*</span>std.error,</span>
<span id="cb144-40"><a href="#cb144-40" tabindex="-1"></a>            <span class="at">Lower =</span> predicted <span class="sc">-</span> <span class="fl">1.41</span><span class="sc">*</span>std.error, </span>
<span id="cb144-41"><a href="#cb144-41" tabindex="-1"></a>            <span class="at">Data =</span> <span class="st">&quot;CSES&quot;</span>)</span>
<span id="cb144-42"><a href="#cb144-42" tabindex="-1"></a></span>
<span id="cb144-43"><a href="#cb144-43" tabindex="-1"></a></span>
<span id="cb144-44"><a href="#cb144-44" tabindex="-1"></a></span>
<span id="cb144-45"><a href="#cb144-45" tabindex="-1"></a></span>
<span id="cb144-46"><a href="#cb144-46" tabindex="-1"></a></span>
<span id="cb144-47"><a href="#cb144-47" tabindex="-1"></a></span>
<span id="cb144-48"><a href="#cb144-48" tabindex="-1"></a>Euro_hyp4_fig <span class="ot">&lt;-</span> sjPlot<span class="sc">::</span><span class="fu">plot_model</span>(Euro_hyp4,</span>
<span id="cb144-49"><a href="#cb144-49" tabindex="-1"></a>                                         <span class="at">type =</span> <span class="st">&quot;int&quot;</span>, <span class="at">mdrt.values =</span> <span class="st">&quot;meansd&quot;</span>)</span>
<span id="cb144-50"><a href="#cb144-50" tabindex="-1"></a></span>
<span id="cb144-51"><a href="#cb144-51" tabindex="-1"></a>Euro_hyp4_fig_data <span class="ot">&lt;-</span> Euro_hyp4_fig<span class="sc">$</span>data</span>
<span id="cb144-52"><a href="#cb144-52" tabindex="-1"></a></span>
<span id="cb144-53"><a href="#cb144-53" tabindex="-1"></a></span>
<span id="cb144-54"><a href="#cb144-54" tabindex="-1"></a>Euro_hyp4_fig_data <span class="ot">&lt;-</span> Euro_hyp4_fig_data <span class="sc">%&gt;%</span></span>
<span id="cb144-55"><a href="#cb144-55" tabindex="-1"></a>  <span class="fu">mutate</span>( <span class="at">Quota =</span> <span class="fu">case_when</span>(</span>
<span id="cb144-56"><a href="#cb144-56" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;No quota&quot;</span>,</span>
<span id="cb144-57"><a href="#cb144-57" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Non-effective quota&quot;</span>,</span>
<span id="cb144-58"><a href="#cb144-58" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Effective quota&quot;</span>),</span>
<span id="cb144-59"><a href="#cb144-59" tabindex="-1"></a>          <span class="at">Corruption =</span> <span class="fu">case_when</span>(</span>
<span id="cb144-60"><a href="#cb144-60" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="sc">-</span><span class="fl">0.02</span> <span class="sc">~</span> <span class="st">&quot;Mean - 1sd (Less corrupt)&quot;</span>,</span>
<span id="cb144-61"><a href="#cb144-61" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="fl">0.13</span> <span class="sc">~</span> <span class="st">&quot;Mean&quot;</span>,</span>
<span id="cb144-62"><a href="#cb144-62" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="fl">0.27</span> <span class="sc">~</span> <span class="st">&quot;Mean + 1sd (More corrupt)&quot;</span>),</span>
<span id="cb144-63"><a href="#cb144-63" tabindex="-1"></a>          <span class="at">Corruption =</span> <span class="fu">as.factor</span>(Corruption),</span>
<span id="cb144-64"><a href="#cb144-64" tabindex="-1"></a>            <span class="at">Upper =</span> predicted <span class="sc">+</span> <span class="fl">1.41</span><span class="sc">*</span>std.error,</span>
<span id="cb144-65"><a href="#cb144-65" tabindex="-1"></a>            <span class="at">Lower =</span> predicted <span class="sc">-</span> <span class="fl">1.41</span><span class="sc">*</span>std.error, </span>
<span id="cb144-66"><a href="#cb144-66" tabindex="-1"></a>            <span class="at">Data =</span> <span class="st">&quot;Eurobarometer&quot;</span>)</span>
<span id="cb144-67"><a href="#cb144-67" tabindex="-1"></a></span>
<span id="cb144-68"><a href="#cb144-68" tabindex="-1"></a></span>
<span id="cb144-69"><a href="#cb144-69" tabindex="-1"></a></span>
<span id="cb144-70"><a href="#cb144-70" tabindex="-1"></a>Latino_hyp4_fig <span class="ot">&lt;-</span> sjPlot<span class="sc">::</span><span class="fu">plot_model</span>(Latino_hyp4,</span>
<span id="cb144-71"><a href="#cb144-71" tabindex="-1"></a>                                         <span class="at">type =</span> <span class="st">&quot;int&quot;</span>, <span class="at">mdrt.values =</span> <span class="st">&quot;meansd&quot;</span>)</span>
<span id="cb144-72"><a href="#cb144-72" tabindex="-1"></a></span>
<span id="cb144-73"><a href="#cb144-73" tabindex="-1"></a>Latino_hyp4_fig_data <span class="ot">&lt;-</span> Latino_hyp4_fig<span class="sc">$</span>data</span>
<span id="cb144-74"><a href="#cb144-74" tabindex="-1"></a></span>
<span id="cb144-75"><a href="#cb144-75" tabindex="-1"></a></span>
<span id="cb144-76"><a href="#cb144-76" tabindex="-1"></a>Latino_hyp4_fig_data <span class="ot">&lt;-</span> Latino_hyp4_fig_data <span class="sc">%&gt;%</span></span>
<span id="cb144-77"><a href="#cb144-77" tabindex="-1"></a>  <span class="fu">mutate</span>( <span class="at">Quota =</span> <span class="fu">case_when</span>(</span>
<span id="cb144-78"><a href="#cb144-78" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">&quot;No quota&quot;</span>,</span>
<span id="cb144-79"><a href="#cb144-79" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">&quot;Non-effective quota&quot;</span>,</span>
<span id="cb144-80"><a href="#cb144-80" tabindex="-1"></a>                           x <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">&quot;Effective quota&quot;</span>),</span>
<span id="cb144-81"><a href="#cb144-81" tabindex="-1"></a>          <span class="at">Corruption =</span> <span class="fu">case_when</span>(</span>
<span id="cb144-82"><a href="#cb144-82" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="fl">0.25</span> <span class="sc">~</span> <span class="st">&quot;Mean - 1sd (Less corrupt)&quot;</span>,</span>
<span id="cb144-83"><a href="#cb144-83" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="fl">0.54</span> <span class="sc">~</span> <span class="st">&quot;Mean&quot;</span>,</span>
<span id="cb144-84"><a href="#cb144-84" tabindex="-1"></a>                           group <span class="sc">==</span> <span class="fl">0.84</span> <span class="sc">~</span> <span class="st">&quot;Mean + 1sd (More corrupt)&quot;</span>),</span>
<span id="cb144-85"><a href="#cb144-85" tabindex="-1"></a>          <span class="at">Corruption =</span> <span class="fu">as.factor</span>(Corruption),</span>
<span id="cb144-86"><a href="#cb144-86" tabindex="-1"></a>            <span class="at">Upper =</span> predicted <span class="sc">+</span> <span class="fl">1.41</span><span class="sc">*</span>std.error,</span>
<span id="cb144-87"><a href="#cb144-87" tabindex="-1"></a>            <span class="at">Lower =</span> predicted <span class="sc">-</span> <span class="fl">1.41</span><span class="sc">*</span>std.error, </span>
<span id="cb144-88"><a href="#cb144-88" tabindex="-1"></a>            <span class="at">Data =</span> <span class="st">&quot;Latinobarometer&quot;</span>)</span>
<span id="cb144-89"><a href="#cb144-89" tabindex="-1"></a></span>
<span id="cb144-90"><a href="#cb144-90" tabindex="-1"></a></span>
<span id="cb144-91"><a href="#cb144-91" tabindex="-1"></a>hyp_4_fig_data <span class="ot">&lt;-</span> <span class="fu">rbind</span>(Americas_hyp4_fig_data,</span>
<span id="cb144-92"><a href="#cb144-92" tabindex="-1"></a>      CSES_hyp4_fig_data,</span>
<span id="cb144-93"><a href="#cb144-93" tabindex="-1"></a>      Euro_hyp4_fig_data,</span>
<span id="cb144-94"><a href="#cb144-94" tabindex="-1"></a>      Latino_hyp4_fig_data)</span>
<span id="cb144-95"><a href="#cb144-95" tabindex="-1"></a></span>
<span id="cb144-96"><a href="#cb144-96" tabindex="-1"></a></span>
<span id="cb144-97"><a href="#cb144-97" tabindex="-1"></a>hyp_4_fig_data<span class="sc">$</span>Quota <span class="ot">&lt;-</span> <span class="fu">as.factor</span>(hyp_4_fig_data<span class="sc">$</span>Quota)</span>
<span id="cb144-98"><a href="#cb144-98" tabindex="-1"></a></span>
<span id="cb144-99"><a href="#cb144-99" tabindex="-1"></a>hyp_4_fig_data<span class="sc">$</span>Quota <span class="ot">&lt;-</span> <span class="fu">factor</span>(hyp_4_fig_data<span class="sc">$</span>Quota , <span class="at">levels =</span> <span class="fu">c</span>(<span class="st">&quot;No quota&quot;</span>, <span class="st">&quot;Non-effective quota&quot;</span>, <span class="st">&quot;Effective quota&quot;</span>))</span>
<span id="cb144-100"><a href="#cb144-100" tabindex="-1"></a></span>
<span id="cb144-101"><a href="#cb144-101" tabindex="-1"></a></span>
<span id="cb144-102"><a href="#cb144-102" tabindex="-1"></a></span>
<span id="cb144-103"><a href="#cb144-103" tabindex="-1"></a>hyp_4_fig_data<span class="sc">$</span>Corruption <span class="ot">&lt;-</span> <span class="fu">as.factor</span>(hyp_4_fig_data<span class="sc">$</span>Corruption)</span>
<span id="cb144-104"><a href="#cb144-104" tabindex="-1"></a></span>
<span id="cb144-105"><a href="#cb144-105" tabindex="-1"></a>hyp_4_fig_data<span class="sc">$</span>Corruption <span class="ot">&lt;-</span> <span class="fu">factor</span>(hyp_4_fig_data<span class="sc">$</span>Corruption , <span class="at">levels =</span> <span class="fu">c</span>(<span class="st">&quot;Mean - 1sd (Less corrupt)&quot;</span>, <span class="st">&quot;Mean&quot;</span>, <span class="st">&quot;Mean + 1sd (More corrupt)&quot;</span>))</span>
<span id="cb144-106"><a href="#cb144-106" tabindex="-1"></a></span>
<span id="cb144-107"><a href="#cb144-107" tabindex="-1"></a></span>
<span id="cb144-108"><a href="#cb144-108" tabindex="-1"></a>hyp_4_fig_data <span class="ot">&lt;-</span> <span class="fu">na.omit</span>(hyp_4_fig_data)</span></code></pre></div>
<div class="sourceCode" id="cb145"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb145-1"><a href="#cb145-1" tabindex="-1"></a><span class="fu">ggplot</span>(hyp_4_fig_data, <span class="fu">aes</span>(<span class="at">x=</span>Quota, <span class="at">y=</span>predicted, <span class="at">fill =</span> Corruption)) <span class="sc">+</span> </span>
<span id="cb145-2"><a href="#cb145-2" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">stat =</span> <span class="st">&quot;identity&quot;</span>,<span class="at">position =</span> <span class="st">&quot;dodge&quot;</span>, <span class="at">width=</span>.<span class="dv">9</span>) <span class="sc">+</span> </span>
<span id="cb145-3"><a href="#cb145-3" tabindex="-1"></a>      <span class="fu">scale_x_discrete</span>(<span class="at">limits=</span>rev) <span class="sc">+</span></span>
<span id="cb145-4"><a href="#cb145-4" tabindex="-1"></a></span>
<span id="cb145-5"><a href="#cb145-5" tabindex="-1"></a>  <span class="fu">ylim</span>(<span class="dv">0</span>,<span class="fl">0.65</span>) <span class="sc">+</span></span>
<span id="cb145-6"><a href="#cb145-6" tabindex="-1"></a>       <span class="fu">geom_text</span>(<span class="fu">aes</span>(<span class="at">label=</span><span class="fu">sprintf</span>(<span class="st">&quot;%0.3f&quot;</span>, predicted)), <span class="at">position=</span><span class="fu">position_dodge</span>(<span class="at">width=</span><span class="fl">0.9</span>), <span class="at">hjust =</span> <span class="fl">2.5</span>, <span class="at">vjust=</span><span class="dv">0</span>, </span>
<span id="cb145-7"><a href="#cb145-7" tabindex="-1"></a>                 <span class="at">size=</span><span class="fl">3.1</span>,</span>
<span id="cb145-8"><a href="#cb145-8" tabindex="-1"></a>                 <span class="at">colour =</span> <span class="st">&quot;white&quot;</span>, <span class="at">fontface =</span> <span class="st">&quot;bold&quot;</span>) <span class="sc">+</span> </span>
<span id="cb145-9"><a href="#cb145-9" tabindex="-1"></a>  </span>
<span id="cb145-10"><a href="#cb145-10" tabindex="-1"></a>   <span class="fu">geom_text</span>(<span class="fu">aes</span>(<span class="at">label=</span><span class="fu">sprintf</span>(<span class="st">&quot;(%0.3f, %0.3f)&quot;</span>, Lower, Upper)), <span class="at">position=</span><span class="fu">position_dodge</span>(<span class="at">width=</span><span class="fl">0.9</span>), <span class="at">hjust =</span> <span class="fl">1.6</span>, <span class="at">vjust =</span> <span class="dv">2</span>, </span>
<span id="cb145-11"><a href="#cb145-11" tabindex="-1"></a>                 <span class="at">size=</span><span class="fl">2.1</span>, </span>
<span id="cb145-12"><a href="#cb145-12" tabindex="-1"></a>               <span class="at">colour =</span> <span class="st">&quot;white&quot;</span>, <span class="at">fontface =</span> <span class="st">&quot;bold&quot;</span>) <span class="sc">+</span> </span>
<span id="cb145-13"><a href="#cb145-13" tabindex="-1"></a></span>
<span id="cb145-14"><a href="#cb145-14" tabindex="-1"></a>  </span>
<span id="cb145-15"><a href="#cb145-15" tabindex="-1"></a>  <span class="fu">geom_errorbar</span>(<span class="fu">aes</span>(<span class="at">ymin=</span>Lower, <span class="at">ymax=</span>Upper), <span class="at">width=</span><span class="dv">0</span>,</span>
<span id="cb145-16"><a href="#cb145-16" tabindex="-1"></a>                 <span class="at">position=</span><span class="fu">position_dodge</span>(.<span class="dv">9</span>)) <span class="sc">+</span> </span>
<span id="cb145-17"><a href="#cb145-17" tabindex="-1"></a>    <span class="fu">scale_fill_grey</span>(<span class="at">start =</span> <span class="fl">0.75</span>, <span class="at">end =</span> .<span class="dv">35</span>,</span>
<span id="cb145-18"><a href="#cb145-18" tabindex="-1"></a>                   <span class="at">guide =</span> <span class="fu">guide_legend</span>(<span class="at">reverse =</span> <span class="cn">FALSE</span>))  <span class="sc">+</span></span>
<span id="cb145-19"><a href="#cb145-19" tabindex="-1"></a></span>
<span id="cb145-20"><a href="#cb145-20" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">y =</span> <span class="st">&quot;Predicted Satisfaction with Democracy&quot;</span>, </span>
<span id="cb145-21"><a href="#cb145-21" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Quota type&quot;</span>,</span>
<span id="cb145-22"><a href="#cb145-22" tabindex="-1"></a>       <span class="at">title =</span> <span class="st">&quot;&quot;</span>) <span class="sc">+</span></span>
<span id="cb145-23"><a href="#cb145-23" tabindex="-1"></a>    <span class="fu">theme_bw</span>(<span class="dv">12</span>) <span class="sc">+</span></span>
<span id="cb145-24"><a href="#cb145-24" tabindex="-1"></a>    <span class="fu">facet_wrap</span>(<span class="sc">~</span>Data) <span class="sc">+</span> </span>
<span id="cb145-25"><a href="#cb145-25" tabindex="-1"></a></span>
<span id="cb145-26"><a href="#cb145-26" tabindex="-1"></a></span>
<span id="cb145-27"><a href="#cb145-27" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position=</span><span class="st">&quot;bottom&quot;</span>, <span class="at">legend.title =</span> <span class="fu">element_blank</span>()) <span class="sc">+</span> </span>
<span id="cb145-28"><a href="#cb145-28" tabindex="-1"></a>  <span class="fu">coord_flip</span>()</span></code></pre></div>
<pre><code>## Warning in geom_col(stat = &quot;identity&quot;, position = &quot;dodge&quot;, width = 0.9):
## Ignoring unknown parameters: `stat`</code></pre>
<p><img 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" width="672" /></p>
<div class="sourceCode" id="cb147"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb147-1"><a href="#cb147-1" tabindex="-1"></a> <span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_5.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">11</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
</div>
</div>
<div id="predicted-comparisons-and-contrasts" class="section level2">
<h2>Predicted Comparisons and Contrasts</h2>
<p>First and second difference comparisons for relevant quantities from
the text using FDR adjusted p-values. We make use of the emmeans package
to generate predictions and differences for our figures</p>
<div id="hypothesis-1-comparisons" class="section level3">
<h3>Hypothesis 1 comparisons</h3>
<div class="sourceCode" id="cb148"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb148-1"><a href="#cb148-1" tabindex="-1"></a><span class="co">#bring in the loaded models </span></span>
<span id="cb148-2"><a href="#cb148-2" tabindex="-1"></a>Americas_hyp1 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Americas_hyp1.rds&quot;</span>)</span>
<span id="cb148-3"><a href="#cb148-3" tabindex="-1"></a>CSES_hyp1 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;CSES_hyp1.rds&quot;</span>)</span>
<span id="cb148-4"><a href="#cb148-4" tabindex="-1"></a>Euro_hyp1 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Euro_hyp1.rds&quot;</span>)</span>
<span id="cb148-5"><a href="#cb148-5" tabindex="-1"></a>Latino_hyp1 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Latino_hyp1.rds&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb149"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb149-1"><a href="#cb149-1" tabindex="-1"></a>Americas.base.emm <span class="ot">&lt;-</span> <span class="fu">emmeans</span>(Americas_hyp1,</span>
<span id="cb149-2"><a href="#cb149-2" tabindex="-1"></a>                                 pairwise <span class="sc">~</span> quota_effect,</span>
<span id="cb149-3"><a href="#cb149-3" tabindex="-1"></a>                                 <span class="at">adjust =</span> <span class="st">&quot;fdr&quot;</span>)</span></code></pre></div>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;pbkrtest.limit = 107527&#39; (or larger)
## [or, globally, &#39;set emm_options(pbkrtest.limit = 107527)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;lmerTest.limit = 107527&#39; (or larger)
## [or, globally, &#39;set emm_options(lmerTest.limit = 107527)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<div class="sourceCode" id="cb152"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb152-1"><a href="#cb152-1" tabindex="-1"></a><span class="fu">summary</span>(Americas.base.emm)</span></code></pre></div>
<pre><code>## $emmeans
##  quota_effect  emmean     SE  df asymp.LCL asymp.UCL
##  No Quota       0.511 0.0093 Inf     0.492     0.529
##  Non-Effective  0.540 0.0124 Inf     0.516     0.564
##  Effective      0.495 0.0115 Inf     0.472     0.518
## 
## Results are averaged over the levels of: Female 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast                    estimate     SE  df z.ratio p.value
##  No Quota - (Non-Effective)   -0.0295 0.0152 Inf  -1.934  0.0796
##  No Quota - Effective          0.0156 0.0164 Inf   0.955  0.3395
##  (Non-Effective) - Effective   0.0451 0.0184 Inf   2.453  0.0425
## 
## Results are averaged over the levels of: Female 
## Degrees-of-freedom method: asymptotic 
## P value adjustment: fdr method for 3 tests</code></pre>
<div class="sourceCode" id="cb154"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb154-1"><a href="#cb154-1" tabindex="-1"></a>CSES.base.emm <span class="ot">&lt;-</span> <span class="fu">emmeans</span>(CSES_hyp1,</span>
<span id="cb154-2"><a href="#cb154-2" tabindex="-1"></a>                                 pairwise <span class="sc">~</span> quota_effect,</span>
<span id="cb154-3"><a href="#cb154-3" tabindex="-1"></a>                                 <span class="at">adjust =</span> <span class="st">&quot;fdr&quot;</span>)</span></code></pre></div>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;pbkrtest.limit = 156300&#39; (or larger)
## [or, globally, &#39;set emm_options(pbkrtest.limit = 156300)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;lmerTest.limit = 156300&#39; (or larger)
## [or, globally, &#39;set emm_options(lmerTest.limit = 156300)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<div class="sourceCode" id="cb157"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb157-1"><a href="#cb157-1" tabindex="-1"></a><span class="fu">summary</span>(CSES.base.emm)</span></code></pre></div>
<pre><code>## $emmeans
##  quota_effect  emmean      SE  df asymp.LCL asymp.UCL
##  No Quota       0.543 0.00816 Inf     0.527     0.559
##  Non-Effective  0.578 0.03012 Inf     0.519     0.637
##  Effective      0.472 0.01975 Inf     0.433     0.510
## 
## Results are averaged over the levels of: Female 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast                    estimate     SE  df z.ratio p.value
##  No Quota - (Non-Effective)   -0.0357 0.0315 Inf  -1.134  0.2568
##  No Quota - Effective          0.0710 0.0215 Inf   3.300  0.0029
##  (Non-Effective) - Effective   0.1066 0.0349 Inf   3.058  0.0033
## 
## Results are averaged over the levels of: Female 
## Degrees-of-freedom method: asymptotic 
## P value adjustment: fdr method for 3 tests</code></pre>
<div class="sourceCode" id="cb159"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb159-1"><a href="#cb159-1" tabindex="-1"></a>Latino.base.emm <span class="ot">&lt;-</span> <span class="fu">emmeans</span>(Latino_hyp1,</span>
<span id="cb159-2"><a href="#cb159-2" tabindex="-1"></a>                                 pairwise <span class="sc">~</span> quota_effect,</span>
<span id="cb159-3"><a href="#cb159-3" tabindex="-1"></a>                                 <span class="at">adjust =</span> <span class="st">&quot;fdr&quot;</span>)</span></code></pre></div>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;pbkrtest.limit = 274148&#39; (or larger)
## [or, globally, &#39;set emm_options(pbkrtest.limit = 274148)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;lmerTest.limit = 274148&#39; (or larger)
## [or, globally, &#39;set emm_options(lmerTest.limit = 274148)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<div class="sourceCode" id="cb162"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb162-1"><a href="#cb162-1" tabindex="-1"></a><span class="fu">summary</span>(Latino.base.emm)</span></code></pre></div>
<pre><code>## $emmeans
##  quota_effect  emmean      SE  df asymp.LCL asymp.UCL
##  No Quota       0.501 0.00986 Inf     0.482     0.521
##  Non-Effective  0.476 0.01505 Inf     0.446     0.505
##  Effective      0.434 0.01310 Inf     0.409     0.460
## 
## Results are averaged over the levels of: Female 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast                    estimate     SE  df z.ratio p.value
##  No Quota - (Non-Effective)    0.0256 0.0180 Inf   1.424  0.1546
##  No Quota - Effective          0.0670 0.0179 Inf   3.745  0.0005
##  (Non-Effective) - Effective   0.0414 0.0205 Inf   2.018  0.0654
## 
## Results are averaged over the levels of: Female 
## Degrees-of-freedom method: asymptotic 
## P value adjustment: fdr method for 3 tests</code></pre>
<div class="sourceCode" id="cb164"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb164-1"><a href="#cb164-1" tabindex="-1"></a>Euro.base.emm <span class="ot">&lt;-</span> <span class="fu">emmeans</span>(Euro_hyp1,</span>
<span id="cb164-2"><a href="#cb164-2" tabindex="-1"></a>                                 pairwise <span class="sc">~</span> quota_effect,</span>
<span id="cb164-3"><a href="#cb164-3" tabindex="-1"></a>                                 <span class="at">adjust =</span> <span class="st">&quot;fdr&quot;</span>)</span></code></pre></div>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;pbkrtest.limit = 581321&#39; (or larger)
## [or, globally, &#39;set emm_options(pbkrtest.limit = 581321)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;lmerTest.limit = 581321&#39; (or larger)
## [or, globally, &#39;set emm_options(lmerTest.limit = 581321)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<div class="sourceCode" id="cb167"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb167-1"><a href="#cb167-1" tabindex="-1"></a><span class="fu">summary</span>(Euro.base.emm)</span></code></pre></div>
<pre><code>## $emmeans
##  quota_effect  emmean      SE  df asymp.LCL asymp.UCL
##  No Quota       0.519 0.00353 Inf     0.512     0.526
##  Non-Effective  0.506 0.01995 Inf     0.467     0.545
##  Effective      0.438 0.01471 Inf     0.410     0.467
## 
## Results are averaged over the levels of: Female 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast                    estimate     SE  df z.ratio p.value
##  No Quota - (Non-Effective)    0.0125 0.0202 Inf   0.619  0.5357
##  No Quota - Effective          0.0804 0.0151 Inf   5.338  &lt;.0001
##  (Non-Effective) - Effective   0.0679 0.0245 Inf   2.768  0.0085
## 
## Results are averaged over the levels of: Female 
## Degrees-of-freedom method: asymptotic 
## P value adjustment: fdr method for 3 tests</code></pre>
</div>
<div id="hypothesis-2-comparisons" class="section level3">
<h3>Hypothesis 2 comparisons</h3>
<div class="sourceCode" id="cb169"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb169-1"><a href="#cb169-1" tabindex="-1"></a><span class="co">#bring in the loaded models </span></span>
<span id="cb169-2"><a href="#cb169-2" tabindex="-1"></a>Americas_hyp2 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Americas_hyp2.rds&quot;</span>)</span>
<span id="cb169-3"><a href="#cb169-3" tabindex="-1"></a>CSES_hyp2 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;CSES_hyp2.rds&quot;</span>)</span>
<span id="cb169-4"><a href="#cb169-4" tabindex="-1"></a>Euro_hyp2 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Euro_hyp2.rds&quot;</span>)</span>
<span id="cb169-5"><a href="#cb169-5" tabindex="-1"></a>Latino_hyp2 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Latino_hyp2.rds&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb170"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb170-1"><a href="#cb170-1" tabindex="-1"></a>Americas.female.emm <span class="ot">&lt;-</span> <span class="fu">emmeans</span>(Americas_hyp2, <span class="st">&quot;quota_effect&quot;</span>)</span></code></pre></div>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;pbkrtest.limit = 107527&#39; (or larger)
## [or, globally, &#39;set emm_options(pbkrtest.limit = 107527)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;lmerTest.limit = 107527&#39; (or larger)
## [or, globally, &#39;set emm_options(lmerTest.limit = 107527)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<pre><code>## NOTE: Results may be misleading due to involvement in interactions</code></pre>
<div class="sourceCode" id="cb174"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb174-1"><a href="#cb174-1" tabindex="-1"></a>Americas.female.emm <span class="ot">&lt;-</span> <span class="fu">emmeans</span>(Americas_hyp2, pairwise <span class="sc">~</span> quota_effect <span class="sc">|</span> Female, <span class="at">adjust =</span> <span class="st">&quot;fdr&quot;</span>)</span></code></pre></div>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;pbkrtest.limit = 107527&#39; (or larger)
## [or, globally, &#39;set emm_options(pbkrtest.limit = 107527)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;lmerTest.limit = 107527&#39; (or larger)
## [or, globally, &#39;set emm_options(lmerTest.limit = 107527)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<div class="sourceCode" id="cb177"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb177-1"><a href="#cb177-1" tabindex="-1"></a><span class="fu">summary</span>(Americas.female.emm)</span></code></pre></div>
<pre><code>## $emmeans
## Female = Man:
##  quota_effect  emmean      SE  df asymp.LCL asymp.UCL
##  No Quota       0.513 0.00953 Inf     0.494     0.531
##  Non-Effective  0.543 0.01265 Inf     0.519     0.568
##  Effective      0.500 0.01171 Inf     0.477     0.523
## 
## Female = Woman:
##  quota_effect  emmean      SE  df asymp.LCL asymp.UCL
##  No Quota       0.510 0.00927 Inf     0.492     0.528
##  Non-Effective  0.535 0.01231 Inf     0.511     0.559
##  Effective      0.491 0.01145 Inf     0.469     0.514
## 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95 
## 
## $contrasts
## Female = Man:
##  contrast                    estimate     SE  df z.ratio p.value
##  No Quota - (Non-Effective)   -0.0308 0.0156 Inf  -1.972  0.0729
##  No Quota - Effective          0.0129 0.0166 Inf   0.777  0.4370
##  (Non-Effective) - Effective   0.0437 0.0187 Inf   2.338  0.0582
## 
## Female = Woman:
##  contrast                    estimate     SE  df z.ratio p.value
##  No Quota - (Non-Effective)   -0.0251 0.0152 Inf  -1.654  0.1473
##  No Quota - Effective          0.0188 0.0163 Inf   1.152  0.2493
##  (Non-Effective) - Effective   0.0439 0.0183 Inf   2.397  0.0496
## 
## Degrees-of-freedom method: asymptotic 
## P value adjustment: fdr method for 3 tests</code></pre>
<div class="sourceCode" id="cb179"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb179-1"><a href="#cb179-1" tabindex="-1"></a>Americas_female_diffs <span class="ot">&lt;-</span> <span class="fu">pairs</span>(<span class="fu">pairs</span>(Americas.female.emm), <span class="at">by =</span> <span class="cn">NULL</span>, <span class="at">adjust =</span> <span class="st">&quot;fdr&quot;</span> )</span>
<span id="cb179-2"><a href="#cb179-2" tabindex="-1"></a><span class="fu">View</span>(<span class="fu">summary</span>(Americas_female_diffs))</span>
<span id="cb179-3"><a href="#cb179-3" tabindex="-1"></a></span>
<span id="cb179-4"><a href="#cb179-4" tabindex="-1"></a></span>
<span id="cb179-5"><a href="#cb179-5" tabindex="-1"></a></span>
<span id="cb179-6"><a href="#cb179-6" tabindex="-1"></a>CSES.female.emm <span class="ot">&lt;-</span> <span class="fu">emmeans</span>(CSES_hyp2, <span class="st">&quot;quota_effect&quot;</span>)</span></code></pre></div>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;pbkrtest.limit = 156300&#39; (or larger)
## [or, globally, &#39;set emm_options(pbkrtest.limit = 156300)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;lmerTest.limit = 156300&#39; (or larger)
## [or, globally, &#39;set emm_options(lmerTest.limit = 156300)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<pre><code>## NOTE: Results may be misleading due to involvement in interactions</code></pre>
<div class="sourceCode" id="cb183"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb183-1"><a href="#cb183-1" tabindex="-1"></a>CSES_female_diffs <span class="ot">&lt;-</span> <span class="fu">pairs</span>(<span class="fu">pairs</span>(CSES.female.emm), <span class="at">by =</span> <span class="cn">NULL</span>, <span class="at">adjust =</span> <span class="st">&quot;fdr&quot;</span> )</span>
<span id="cb183-2"><a href="#cb183-2" tabindex="-1"></a><span class="fu">View</span>(<span class="fu">summary</span>(CSES_female_diffs))</span>
<span id="cb183-3"><a href="#cb183-3" tabindex="-1"></a></span>
<span id="cb183-4"><a href="#cb183-4" tabindex="-1"></a></span>
<span id="cb183-5"><a href="#cb183-5" tabindex="-1"></a></span>
<span id="cb183-6"><a href="#cb183-6" tabindex="-1"></a></span>
<span id="cb183-7"><a href="#cb183-7" tabindex="-1"></a>Euro.female.emm <span class="ot">&lt;-</span> <span class="fu">emmeans</span>(Euro_hyp2, <span class="st">&quot;quota_effect&quot;</span>)</span></code></pre></div>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;pbkrtest.limit = 581321&#39; (or larger)
## [or, globally, &#39;set emm_options(pbkrtest.limit = 581321)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;lmerTest.limit = 581321&#39; (or larger)
## [or, globally, &#39;set emm_options(lmerTest.limit = 581321)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<pre><code>## NOTE: Results may be misleading due to involvement in interactions</code></pre>
<div class="sourceCode" id="cb187"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb187-1"><a href="#cb187-1" tabindex="-1"></a>Euro_female_diffs <span class="ot">&lt;-</span> <span class="fu">pairs</span>(<span class="fu">pairs</span>(Euro.female.emm), <span class="at">by =</span> <span class="cn">NULL</span>, <span class="at">adjust =</span> <span class="st">&quot;fdr&quot;</span> )</span>
<span id="cb187-2"><a href="#cb187-2" tabindex="-1"></a><span class="fu">View</span>(<span class="fu">summary</span>(Euro_female_diffs))</span>
<span id="cb187-3"><a href="#cb187-3" tabindex="-1"></a></span>
<span id="cb187-4"><a href="#cb187-4" tabindex="-1"></a></span>
<span id="cb187-5"><a href="#cb187-5" tabindex="-1"></a></span>
<span id="cb187-6"><a href="#cb187-6" tabindex="-1"></a>Latino.female.emm <span class="ot">&lt;-</span> <span class="fu">emmeans</span>(Latino_hyp2, <span class="st">&quot;quota_effect&quot;</span>)</span></code></pre></div>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;pbkrtest.limit = 274148&#39; (or larger)
## [or, globally, &#39;set emm_options(pbkrtest.limit = 274148)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;lmerTest.limit = 274148&#39; (or larger)
## [or, globally, &#39;set emm_options(lmerTest.limit = 274148)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<pre><code>## NOTE: Results may be misleading due to involvement in interactions</code></pre>
<div class="sourceCode" id="cb191"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb191-1"><a href="#cb191-1" tabindex="-1"></a>Latino_female_diffs <span class="ot">&lt;-</span> <span class="fu">pairs</span>(<span class="fu">pairs</span>(Latino.female.emm), <span class="at">by =</span> <span class="cn">NULL</span>, <span class="at">adjust =</span> <span class="st">&quot;fdr&quot;</span> )</span>
<span id="cb191-2"><a href="#cb191-2" tabindex="-1"></a><span class="fu">View</span>(<span class="fu">summary</span>(Latino_female_diffs))</span></code></pre></div>
</div>
<div id="hypothesis-3a-comparisons-ideology" class="section level3">
<h3>Hypothesis 3a comparisons-Ideology</h3>
<div class="sourceCode" id="cb192"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb192-1"><a href="#cb192-1" tabindex="-1"></a><span class="co">#bring in the loaded models </span></span>
<span id="cb192-2"><a href="#cb192-2" tabindex="-1"></a>Americas_hyp3a <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Americas_hyp3a.rds&quot;</span>)</span>
<span id="cb192-3"><a href="#cb192-3" tabindex="-1"></a>CSES_hyp3a <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;CSES_hyp3a.rds&quot;</span>)</span>
<span id="cb192-4"><a href="#cb192-4" tabindex="-1"></a>Euro_hyp3a <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Euro_hyp3a.rds&quot;</span>)</span>
<span id="cb192-5"><a href="#cb192-5" tabindex="-1"></a>Latino_hyp3a <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Latino_hyp3a.rds&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb193"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb193-1"><a href="#cb193-1" tabindex="-1"></a>Americas.ideology.emm <span class="ot">&lt;-</span> <span class="fu">emmeans</span>(Americas_hyp3a, pairwise <span class="sc">~</span> Ideology <span class="sc">|</span> quota_effect,  <span class="at">at =</span> <span class="fu">list</span>(<span class="at">Ideology =</span> <span class="fu">c</span>(<span class="fl">0.23</span>, <span class="fl">0.52</span>,<span class="fl">0.8</span>)), <span class="at">adjust =</span> <span class="st">&quot;fdr&quot;</span>)</span></code></pre></div>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;pbkrtest.limit = 107527&#39; (or larger)
## [or, globally, &#39;set emm_options(pbkrtest.limit = 107527)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;lmerTest.limit = 107527&#39; (or larger)
## [or, globally, &#39;set emm_options(lmerTest.limit = 107527)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<div class="sourceCode" id="cb196"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb196-1"><a href="#cb196-1" tabindex="-1"></a>Americas_ideology_diffs<span class="ot">&lt;-</span> <span class="fu">pairs</span>(<span class="fu">pairs</span>(Americas.ideology.emm), <span class="at">by =</span> <span class="cn">NULL</span>, <span class="at">adjust =</span> <span class="st">&quot;fdr&quot;</span> )</span>
<span id="cb196-2"><a href="#cb196-2" tabindex="-1"></a>Americas_ideology_diffs_tab <span class="ot">&lt;-</span> <span class="fu">summary</span>(Americas_ideology_diffs)</span>
<span id="cb196-3"><a href="#cb196-3" tabindex="-1"></a><span class="fu">print</span>(Americas_ideology_diffs_tab)</span></code></pre></div>
<pre><code>##  contrast                                                                                
##  (Ideology0.23 - Ideology0.52 No Quota) - (Ideology0.23 - Ideology0.8 No Quota)          
##  (Ideology0.23 - Ideology0.52 No Quota) - (Ideology0.52 - Ideology0.8 No Quota)          
##  (Ideology0.23 - Ideology0.52 No Quota) - (Ideology0.23 - Ideology0.52 Non-Effective)    
##  (Ideology0.23 - Ideology0.52 No Quota) - (Ideology0.23 - Ideology0.8 Non-Effective)     
##  (Ideology0.23 - Ideology0.52 No Quota) - (Ideology0.52 - Ideology0.8 Non-Effective)     
##  (Ideology0.23 - Ideology0.52 No Quota) - (Ideology0.23 - Ideology0.52 Effective)        
##  (Ideology0.23 - Ideology0.52 No Quota) - (Ideology0.23 - Ideology0.8 Effective)         
##  (Ideology0.23 - Ideology0.52 No Quota) - (Ideology0.52 - Ideology0.8 Effective)         
##  (Ideology0.23 - Ideology0.8 No Quota) - (Ideology0.52 - Ideology0.8 No Quota)           
##  (Ideology0.23 - Ideology0.8 No Quota) - (Ideology0.23 - Ideology0.52 Non-Effective)     
##  (Ideology0.23 - Ideology0.8 No Quota) - (Ideology0.23 - Ideology0.8 Non-Effective)      
##  (Ideology0.23 - Ideology0.8 No Quota) - (Ideology0.52 - Ideology0.8 Non-Effective)      
##  (Ideology0.23 - Ideology0.8 No Quota) - (Ideology0.23 - Ideology0.52 Effective)         
##  (Ideology0.23 - Ideology0.8 No Quota) - (Ideology0.23 - Ideology0.8 Effective)          
##  (Ideology0.23 - Ideology0.8 No Quota) - (Ideology0.52 - Ideology0.8 Effective)          
##  (Ideology0.52 - Ideology0.8 No Quota) - (Ideology0.23 - Ideology0.52 Non-Effective)     
##  (Ideology0.52 - Ideology0.8 No Quota) - (Ideology0.23 - Ideology0.8 Non-Effective)      
##  (Ideology0.52 - Ideology0.8 No Quota) - (Ideology0.52 - Ideology0.8 Non-Effective)      
##  (Ideology0.52 - Ideology0.8 No Quota) - (Ideology0.23 - Ideology0.52 Effective)         
##  (Ideology0.52 - Ideology0.8 No Quota) - (Ideology0.23 - Ideology0.8 Effective)          
##  (Ideology0.52 - Ideology0.8 No Quota) - (Ideology0.52 - Ideology0.8 Effective)          
##  (Ideology0.23 - Ideology0.52 Non-Effective) - (Ideology0.23 - Ideology0.8 Non-Effective)
##  (Ideology0.23 - Ideology0.52 Non-Effective) - (Ideology0.52 - Ideology0.8 Non-Effective)
##  (Ideology0.23 - Ideology0.52 Non-Effective) - (Ideology0.23 - Ideology0.52 Effective)   
##  (Ideology0.23 - Ideology0.52 Non-Effective) - (Ideology0.23 - Ideology0.8 Effective)    
##  (Ideology0.23 - Ideology0.52 Non-Effective) - (Ideology0.52 - Ideology0.8 Effective)    
##  (Ideology0.23 - Ideology0.8 Non-Effective) - (Ideology0.52 - Ideology0.8 Non-Effective) 
##  (Ideology0.23 - Ideology0.8 Non-Effective) - (Ideology0.23 - Ideology0.52 Effective)    
##  (Ideology0.23 - Ideology0.8 Non-Effective) - (Ideology0.23 - Ideology0.8 Effective)     
##  (Ideology0.23 - Ideology0.8 Non-Effective) - (Ideology0.52 - Ideology0.8 Effective)     
##  (Ideology0.52 - Ideology0.8 Non-Effective) - (Ideology0.23 - Ideology0.52 Effective)    
##  (Ideology0.52 - Ideology0.8 Non-Effective) - (Ideology0.23 - Ideology0.8 Effective)     
##  (Ideology0.52 - Ideology0.8 Non-Effective) - (Ideology0.52 - Ideology0.8 Effective)     
##  (Ideology0.23 - Ideology0.52 Effective) - (Ideology0.23 - Ideology0.8 Effective)        
##  (Ideology0.23 - Ideology0.52 Effective) - (Ideology0.52 - Ideology0.8 Effective)        
##  (Ideology0.23 - Ideology0.8 Effective) - (Ideology0.52 - Ideology0.8 Effective)         
##   estimate       SE  df z.ratio p.value
##  -0.003164 0.005305 Inf  -0.596  0.5833
##   0.000113 0.000189 Inf   0.596  0.5833
##   0.026853 0.009083 Inf   2.957  0.0122
##   0.049617 0.015241 Inf   3.256  0.0081
##   0.026040 0.008885 Inf   2.931  0.0122
##   0.015047 0.008163 Inf   1.843  0.1014
##   0.026412 0.013078 Inf   2.020  0.0971
##   0.014641 0.008011 Inf   1.828  0.1014
##   0.003277 0.005494 Inf   0.596  0.5833
##   0.030017 0.012997 Inf   2.310  0.0641
##   0.052780 0.017852 Inf   2.957  0.0122
##   0.029204 0.012860 Inf   2.271  0.0641
##   0.018211 0.012372 Inf   1.472  0.2031
##   0.029576 0.016045 Inf   1.843  0.1014
##   0.017805 0.012272 Inf   1.451  0.2033
##   0.026740 0.008969 Inf   2.981  0.0122
##   0.049504 0.015174 Inf   3.262  0.0081
##   0.025927 0.008770 Inf   2.957  0.0122
##   0.014934 0.008037 Inf   1.858  0.1014
##   0.026299 0.012999 Inf   2.023  0.0971
##   0.014528 0.007882 Inf   1.843  0.1014
##   0.022764 0.006984 Inf   3.260  0.0081
##  -0.000813 0.000249 Inf  -3.260  0.0081
##  -0.011806 0.009422 Inf  -1.253  0.2522
##  -0.000441 0.013898 Inf  -0.032  0.9785
##  -0.012212 0.009290 Inf  -1.315  0.2516
##  -0.023577 0.007233 Inf  -3.260  0.0081
##  -0.034569 0.015445 Inf  -2.238  0.0648
##  -0.023205 0.018519 Inf  -1.253  0.2522
##  -0.034975 0.015365 Inf  -2.276  0.0641
##  -0.010993 0.009232 Inf  -1.191  0.2714
##   0.000372 0.013770 Inf   0.027  0.9785
##  -0.011399 0.009097 Inf  -1.253  0.2522
##   0.011365 0.005830 Inf   1.949  0.0971
##  -0.000406 0.000208 Inf  -1.949  0.0971
##  -0.011771 0.006038 Inf  -1.949  0.0971
## 
## Results are averaged over the levels of: Female 
## Degrees-of-freedom method: asymptotic 
## P value adjustment: fdr method for 36 tests</code></pre>
<div class="sourceCode" id="cb198"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb198-1"><a href="#cb198-1" tabindex="-1"></a>CSES.ideology.emm <span class="ot">&lt;-</span> <span class="fu">emmeans</span>(CSES_hyp3a, pairwise <span class="sc">~</span> Ideology <span class="sc">|</span> quota_effect,  <span class="at">at =</span> <span class="fu">list</span>(<span class="at">Ideology =</span> <span class="fu">c</span>(<span class="fl">0.23</span>, <span class="fl">0.52</span>,<span class="fl">0.8</span>)), <span class="at">adjust =</span> <span class="st">&quot;fdr&quot;</span>)</span></code></pre></div>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;pbkrtest.limit = 156300&#39; (or larger)
## [or, globally, &#39;set emm_options(pbkrtest.limit = 156300)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;lmerTest.limit = 156300&#39; (or larger)
## [or, globally, &#39;set emm_options(lmerTest.limit = 156300)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<div class="sourceCode" id="cb201"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb201-1"><a href="#cb201-1" tabindex="-1"></a>CSES_ideology_diffs <span class="ot">&lt;-</span> <span class="fu">pairs</span>(<span class="fu">pairs</span>(CSES.ideology.emm), <span class="at">by =</span> <span class="cn">NULL</span>, <span class="at">adjust =</span> <span class="st">&quot;fdr&quot;</span> )</span>
<span id="cb201-2"><a href="#cb201-2" tabindex="-1"></a>CSES_ideology_diffs_tab <span class="ot">&lt;-</span> <span class="fu">summary</span>(CSES_ideology_diffs)</span>
<span id="cb201-3"><a href="#cb201-3" tabindex="-1"></a></span>
<span id="cb201-4"><a href="#cb201-4" tabindex="-1"></a></span>
<span id="cb201-5"><a href="#cb201-5" tabindex="-1"></a></span>
<span id="cb201-6"><a href="#cb201-6" tabindex="-1"></a>Euro.ideology.emm <span class="ot">&lt;-</span> <span class="fu">emmeans</span>(Euro_hyp3a, pairwise <span class="sc">~</span> Ideology <span class="sc">|</span> quota_effect,  <span class="at">at =</span> <span class="fu">list</span>(<span class="at">Ideology =</span> <span class="fu">c</span>(<span class="fl">0.25</span>, <span class="fl">0.48</span>,<span class="fl">0.72</span>)), <span class="at">adjust =</span> <span class="st">&quot;fdr&quot;</span>)</span></code></pre></div>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;pbkrtest.limit = 581321&#39; (or larger)
## [or, globally, &#39;set emm_options(pbkrtest.limit = 581321)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;lmerTest.limit = 581321&#39; (or larger)
## [or, globally, &#39;set emm_options(lmerTest.limit = 581321)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<div class="sourceCode" id="cb204"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb204-1"><a href="#cb204-1" tabindex="-1"></a>Euro_ideology_diffs <span class="ot">&lt;-</span> <span class="fu">pairs</span>(<span class="fu">pairs</span>(Euro.ideology.emm), <span class="at">by =</span> <span class="cn">NULL</span>, <span class="at">adjust =</span> <span class="st">&quot;fdr&quot;</span> )</span>
<span id="cb204-2"><a href="#cb204-2" tabindex="-1"></a>Euro_ideology_diffs_tab <span class="ot">&lt;-</span> <span class="fu">summary</span>(Euro_ideology_diffs)</span>
<span id="cb204-3"><a href="#cb204-3" tabindex="-1"></a><span class="fu">print</span>(Euro_ideology_diffs_tab)</span></code></pre></div>
<pre><code>##  contrast                                                                                 
##  (Ideology0.25 - Ideology0.48 No Quota) - (Ideology0.25 - Ideology0.72 No Quota)          
##  (Ideology0.25 - Ideology0.48 No Quota) - (Ideology0.48 - Ideology0.72 No Quota)          
##  (Ideology0.25 - Ideology0.48 No Quota) - (Ideology0.25 - Ideology0.48 Non-Effective)     
##  (Ideology0.25 - Ideology0.48 No Quota) - (Ideology0.25 - Ideology0.72 Non-Effective)     
##  (Ideology0.25 - Ideology0.48 No Quota) - (Ideology0.48 - Ideology0.72 Non-Effective)     
##  (Ideology0.25 - Ideology0.48 No Quota) - (Ideology0.25 - Ideology0.48 Effective)         
##  (Ideology0.25 - Ideology0.48 No Quota) - (Ideology0.25 - Ideology0.72 Effective)         
##  (Ideology0.25 - Ideology0.48 No Quota) - (Ideology0.48 - Ideology0.72 Effective)         
##  (Ideology0.25 - Ideology0.72 No Quota) - (Ideology0.48 - Ideology0.72 No Quota)          
##  (Ideology0.25 - Ideology0.72 No Quota) - (Ideology0.25 - Ideology0.48 Non-Effective)     
##  (Ideology0.25 - Ideology0.72 No Quota) - (Ideology0.25 - Ideology0.72 Non-Effective)     
##  (Ideology0.25 - Ideology0.72 No Quota) - (Ideology0.48 - Ideology0.72 Non-Effective)     
##  (Ideology0.25 - Ideology0.72 No Quota) - (Ideology0.25 - Ideology0.48 Effective)         
##  (Ideology0.25 - Ideology0.72 No Quota) - (Ideology0.25 - Ideology0.72 Effective)         
##  (Ideology0.25 - Ideology0.72 No Quota) - (Ideology0.48 - Ideology0.72 Effective)         
##  (Ideology0.48 - Ideology0.72 No Quota) - (Ideology0.25 - Ideology0.48 Non-Effective)     
##  (Ideology0.48 - Ideology0.72 No Quota) - (Ideology0.25 - Ideology0.72 Non-Effective)     
##  (Ideology0.48 - Ideology0.72 No Quota) - (Ideology0.48 - Ideology0.72 Non-Effective)     
##  (Ideology0.48 - Ideology0.72 No Quota) - (Ideology0.25 - Ideology0.48 Effective)         
##  (Ideology0.48 - Ideology0.72 No Quota) - (Ideology0.25 - Ideology0.72 Effective)         
##  (Ideology0.48 - Ideology0.72 No Quota) - (Ideology0.48 - Ideology0.72 Effective)         
##  (Ideology0.25 - Ideology0.48 Non-Effective) - (Ideology0.25 - Ideology0.72 Non-Effective)
##  (Ideology0.25 - Ideology0.48 Non-Effective) - (Ideology0.48 - Ideology0.72 Non-Effective)
##  (Ideology0.25 - Ideology0.48 Non-Effective) - (Ideology0.25 - Ideology0.48 Effective)    
##  (Ideology0.25 - Ideology0.48 Non-Effective) - (Ideology0.25 - Ideology0.72 Effective)    
##  (Ideology0.25 - Ideology0.48 Non-Effective) - (Ideology0.48 - Ideology0.72 Effective)    
##  (Ideology0.25 - Ideology0.72 Non-Effective) - (Ideology0.48 - Ideology0.72 Non-Effective)
##  (Ideology0.25 - Ideology0.72 Non-Effective) - (Ideology0.25 - Ideology0.48 Effective)    
##  (Ideology0.25 - Ideology0.72 Non-Effective) - (Ideology0.25 - Ideology0.72 Effective)    
##  (Ideology0.25 - Ideology0.72 Non-Effective) - (Ideology0.48 - Ideology0.72 Effective)    
##  (Ideology0.48 - Ideology0.72 Non-Effective) - (Ideology0.25 - Ideology0.48 Effective)    
##  (Ideology0.48 - Ideology0.72 Non-Effective) - (Ideology0.25 - Ideology0.72 Effective)    
##  (Ideology0.48 - Ideology0.72 Non-Effective) - (Ideology0.48 - Ideology0.72 Effective)    
##  (Ideology0.25 - Ideology0.48 Effective) - (Ideology0.25 - Ideology0.72 Effective)        
##  (Ideology0.25 - Ideology0.48 Effective) - (Ideology0.48 - Ideology0.72 Effective)        
##  (Ideology0.25 - Ideology0.72 Effective) - (Ideology0.48 - Ideology0.72 Effective)        
##   estimate       SE  df z.ratio p.value
##   2.24e-02 1.64e-03 Inf  13.666  &lt;.0001
##   9.35e-04 6.84e-05 Inf  13.666  &lt;.0001
##   1.61e-04 8.76e-03 Inf   0.018  0.9854
##   2.28e-02 1.77e-02 Inf   1.288  0.2637
##   1.10e-03 9.13e-03 Inf   0.121  0.9854
##  -2.24e-02 6.59e-03 Inf  -3.399  0.0027
##  -2.33e-02 1.32e-02 Inf  -1.772  0.1147
##  -2.24e-02 6.86e-03 Inf  -3.270  0.0039
##  -2.15e-02 1.57e-03 Inf -13.666  &lt;.0001
##  -2.23e-02 9.20e-03 Inf  -2.422  0.0368
##   3.28e-04 1.79e-02 Inf   0.018  0.9854
##  -2.13e-02 9.55e-03 Inf  -2.235  0.0539
##  -4.49e-02 7.17e-03 Inf  -6.260  &lt;.0001
##  -4.58e-02 1.35e-02 Inf  -3.399  0.0027
##  -4.49e-02 7.42e-03 Inf  -6.054  &lt;.0001
##  -7.75e-04 8.77e-03 Inf  -0.088  0.9854
##   2.18e-02 1.77e-02 Inf   1.235  0.2790
##   1.67e-04 9.14e-03 Inf   0.018  0.9854
##  -2.33e-02 6.61e-03 Inf  -3.531  0.0025
##  -2.43e-02 1.32e-02 Inf  -1.842  0.1026
##  -2.34e-02 6.88e-03 Inf  -3.399  0.0027
##   2.26e-02 8.99e-03 Inf   2.514  0.0330
##   9.42e-04 3.75e-04 Inf   2.514  0.0330
##  -2.26e-02 1.07e-02 Inf  -2.102  0.0609
##  -2.35e-02 1.57e-02 Inf  -1.501  0.1848
##  -2.26e-02 1.09e-02 Inf  -2.073  0.0624
##  -2.17e-02 8.62e-03 Inf  -2.514  0.0330
##  -4.52e-02 1.87e-02 Inf  -2.411  0.0368
##  -4.61e-02 2.19e-02 Inf  -2.102  0.0609
##  -4.52e-02 1.88e-02 Inf  -2.401  0.0368
##  -2.35e-02 1.10e-02 Inf  -2.130  0.0609
##  -2.45e-02 1.59e-02 Inf  -1.540  0.1779
##  -2.36e-02 1.12e-02 Inf  -2.102  0.0609
##  -9.41e-04 6.68e-03 Inf  -0.141  0.9854
##  -3.92e-05 2.78e-04 Inf  -0.141  0.9854
##   9.01e-04 6.40e-03 Inf   0.141  0.9854
## 
## Results are averaged over the levels of: Female 
## Degrees-of-freedom method: asymptotic 
## P value adjustment: fdr method for 36 tests</code></pre>
<div class="sourceCode" id="cb206"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb206-1"><a href="#cb206-1" tabindex="-1"></a>Latino.ideology.emm <span class="ot">&lt;-</span> <span class="fu">emmeans</span>(Latino_hyp3a, pairwise <span class="sc">~</span> Ideology <span class="sc">|</span> quota_effect,  <span class="at">at =</span> <span class="fu">list</span>(<span class="at">Ideology =</span> <span class="fu">c</span>(<span class="fl">0.26</span>, <span class="fl">0.53</span>,<span class="fl">0.8</span>)), <span class="at">adjust =</span> <span class="st">&quot;fdr&quot;</span>)</span></code></pre></div>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;pbkrtest.limit = 274148&#39; (or larger)
## [or, globally, &#39;set emm_options(pbkrtest.limit = 274148)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;lmerTest.limit = 274148&#39; (or larger)
## [or, globally, &#39;set emm_options(lmerTest.limit = 274148)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<div class="sourceCode" id="cb209"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb209-1"><a href="#cb209-1" tabindex="-1"></a>Latino_ideology_diffs <span class="ot">&lt;-</span> <span class="fu">pairs</span>(<span class="fu">pairs</span>(Latino.ideology.emm), <span class="at">by =</span> <span class="cn">NULL</span>, <span class="at">adjust =</span> <span class="st">&quot;fdr&quot;</span> )</span>
<span id="cb209-2"><a href="#cb209-2" tabindex="-1"></a>Latino_ideology_diffs_tab <span class="ot">&lt;-</span> <span class="fu">summary</span>(Latino_ideology_diffs)</span>
<span id="cb209-3"><a href="#cb209-3" tabindex="-1"></a><span class="fu">print</span>(Latino_ideology_diffs_tab)</span></code></pre></div>
<pre><code>##  contrast                                                                                
##  (Ideology0.26 - Ideology0.53 No Quota) - (Ideology0.26 - Ideology0.8 No Quota)          
##  (Ideology0.26 - Ideology0.53 No Quota) - (Ideology0.53 - Ideology0.8 No Quota)          
##  (Ideology0.26 - Ideology0.53 No Quota) - (Ideology0.26 - Ideology0.53 Non-Effective)    
##  (Ideology0.26 - Ideology0.53 No Quota) - (Ideology0.26 - Ideology0.8 Non-Effective)     
##  (Ideology0.26 - Ideology0.53 No Quota) - (Ideology0.53 - Ideology0.8 Non-Effective)     
##  (Ideology0.26 - Ideology0.53 No Quota) - (Ideology0.26 - Ideology0.53 Effective)        
##  (Ideology0.26 - Ideology0.53 No Quota) - (Ideology0.26 - Ideology0.8 Effective)         
##  (Ideology0.26 - Ideology0.53 No Quota) - (Ideology0.53 - Ideology0.8 Effective)         
##  (Ideology0.26 - Ideology0.8 No Quota) - (Ideology0.53 - Ideology0.8 No Quota)           
##  (Ideology0.26 - Ideology0.8 No Quota) - (Ideology0.26 - Ideology0.53 Non-Effective)     
##  (Ideology0.26 - Ideology0.8 No Quota) - (Ideology0.26 - Ideology0.8 Non-Effective)      
##  (Ideology0.26 - Ideology0.8 No Quota) - (Ideology0.53 - Ideology0.8 Non-Effective)      
##  (Ideology0.26 - Ideology0.8 No Quota) - (Ideology0.26 - Ideology0.53 Effective)         
##  (Ideology0.26 - Ideology0.8 No Quota) - (Ideology0.26 - Ideology0.8 Effective)          
##  (Ideology0.26 - Ideology0.8 No Quota) - (Ideology0.53 - Ideology0.8 Effective)          
##  (Ideology0.53 - Ideology0.8 No Quota) - (Ideology0.26 - Ideology0.53 Non-Effective)     
##  (Ideology0.53 - Ideology0.8 No Quota) - (Ideology0.26 - Ideology0.8 Non-Effective)      
##  (Ideology0.53 - Ideology0.8 No Quota) - (Ideology0.53 - Ideology0.8 Non-Effective)      
##  (Ideology0.53 - Ideology0.8 No Quota) - (Ideology0.26 - Ideology0.53 Effective)         
##  (Ideology0.53 - Ideology0.8 No Quota) - (Ideology0.26 - Ideology0.8 Effective)          
##  (Ideology0.53 - Ideology0.8 No Quota) - (Ideology0.53 - Ideology0.8 Effective)          
##  (Ideology0.26 - Ideology0.53 Non-Effective) - (Ideology0.26 - Ideology0.8 Non-Effective)
##  (Ideology0.26 - Ideology0.53 Non-Effective) - (Ideology0.53 - Ideology0.8 Non-Effective)
##  (Ideology0.26 - Ideology0.53 Non-Effective) - (Ideology0.26 - Ideology0.53 Effective)   
##  (Ideology0.26 - Ideology0.53 Non-Effective) - (Ideology0.26 - Ideology0.8 Effective)    
##  (Ideology0.26 - Ideology0.53 Non-Effective) - (Ideology0.53 - Ideology0.8 Effective)    
##  (Ideology0.26 - Ideology0.8 Non-Effective) - (Ideology0.53 - Ideology0.8 Non-Effective) 
##  (Ideology0.26 - Ideology0.8 Non-Effective) - (Ideology0.26 - Ideology0.53 Effective)    
##  (Ideology0.26 - Ideology0.8 Non-Effective) - (Ideology0.26 - Ideology0.8 Effective)     
##  (Ideology0.26 - Ideology0.8 Non-Effective) - (Ideology0.53 - Ideology0.8 Effective)     
##  (Ideology0.53 - Ideology0.8 Non-Effective) - (Ideology0.26 - Ideology0.53 Effective)    
##  (Ideology0.53 - Ideology0.8 Non-Effective) - (Ideology0.26 - Ideology0.8 Effective)     
##  (Ideology0.53 - Ideology0.8 Non-Effective) - (Ideology0.53 - Ideology0.8 Effective)     
##  (Ideology0.26 - Ideology0.53 Effective) - (Ideology0.26 - Ideology0.8 Effective)        
##  (Ideology0.26 - Ideology0.53 Effective) - (Ideology0.53 - Ideology0.8 Effective)        
##  (Ideology0.26 - Ideology0.8 Effective) - (Ideology0.53 - Ideology0.8 Effective)         
##   estimate      SE  df z.ratio p.value
##   0.006072 0.00325 Inf   1.867  0.1200
##   0.000000 0.00000 Inf     NaN     NaN
##   0.012097 0.00601 Inf   2.012  0.0973
##   0.030265 0.01062 Inf   2.849  0.0242
##   0.012097 0.00601 Inf   2.012  0.0973
##   0.006467 0.00526 Inf   1.230  0.3282
##   0.019006 0.00888 Inf   2.139  0.0973
##   0.006467 0.00526 Inf   1.230  0.3282
##  -0.006072 0.00325 Inf  -1.867  0.1200
##   0.006025 0.00824 Inf   0.731  0.5066
##   0.024193 0.01202 Inf   2.012  0.0973
##   0.006025 0.00824 Inf   0.731  0.5066
##   0.000395 0.00771 Inf   0.051  0.9591
##   0.012934 0.01052 Inf   1.230  0.3282
##   0.000395 0.00771 Inf   0.051  0.9591
##   0.012097 0.00601 Inf   2.012  0.0973
##   0.030265 0.01062 Inf   2.849  0.0242
##   0.012097 0.00601 Inf   2.012  0.0973
##   0.006467 0.00526 Inf   1.230  0.3282
##   0.019006 0.00888 Inf   2.139  0.0973
##   0.006467 0.00526 Inf   1.230  0.3282
##   0.018168 0.00506 Inf   3.592  0.0054
##   0.000000 0.00000 Inf     NaN     NaN
##  -0.005629 0.00653 Inf  -0.862  0.4752
##   0.006909 0.00969 Inf   0.713  0.5066
##  -0.005629 0.00653 Inf  -0.862  0.4752
##  -0.018168 0.00506 Inf  -3.592  0.0054
##  -0.023798 0.01093 Inf  -2.178  0.0973
##  -0.011259 0.01306 Inf  -0.862  0.4752
##  -0.023798 0.01093 Inf  -2.178  0.0973
##  -0.005629 0.00653 Inf  -0.862  0.4752
##   0.006909 0.00969 Inf   0.713  0.5066
##  -0.005629 0.00653 Inf  -0.862  0.4752
##   0.012539 0.00413 Inf   3.033  0.0200
##   0.000000 0.00000 Inf     NaN     NaN
##  -0.012539 0.00413 Inf  -3.033  0.0200
## 
## Results are averaged over the levels of: Female 
## Degrees-of-freedom method: asymptotic 
## P value adjustment: fdr method for 33 tests</code></pre>
</div>
<div id="hypothesis-3b-comparisons-ideology" class="section level3">
<h3>Hypothesis 3b comparisons-Ideology</h3>
<div class="sourceCode" id="cb211"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb211-1"><a href="#cb211-1" tabindex="-1"></a>Americas_hyp3b <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Americas_hyp3b.rds&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb212"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb212-1"><a href="#cb212-1" tabindex="-1"></a>Americas.support.emm <span class="ot">&lt;-</span> <span class="fu">emmeans</span>(Americas_hyp3b, pairwise <span class="sc">~</span> Quota_support <span class="sc">|</span> quota_effect,  <span class="at">at =</span> <span class="fu">list</span>(<span class="at">Quota_support =</span> <span class="fu">c</span>(<span class="fl">0.37</span>, <span class="fl">0.68</span>,<span class="fl">0.99</span>)), <span class="at">adjust =</span> <span class="st">&quot;fdr&quot;</span>)</span></code></pre></div>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;pbkrtest.limit = 13648&#39; (or larger)
## [or, globally, &#39;set emm_options(pbkrtest.limit = 13648)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;lmerTest.limit = 13648&#39; (or larger)
## [or, globally, &#39;set emm_options(lmerTest.limit = 13648)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<div class="sourceCode" id="cb215"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb215-1"><a href="#cb215-1" tabindex="-1"></a>Support_americas_diffs <span class="ot">&lt;-</span> <span class="fu">pairs</span>(<span class="fu">pairs</span>(Americas.support.emm), <span class="at">by =</span> <span class="cn">NULL</span>, <span class="at">adjust =</span> <span class="st">&quot;fdr&quot;</span> )</span>
<span id="cb215-2"><a href="#cb215-2" tabindex="-1"></a>Support_americas_diffs_tab <span class="ot">&lt;-</span> <span class="fu">summary</span>(Support_americas_diffs)</span></code></pre></div>
</div>
<div id="hypothesis-4-comparisons-corruption" class="section level3">
<h3>Hypothesis 4 comparisons-Corruption</h3>
<div class="sourceCode" id="cb216"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb216-1"><a href="#cb216-1" tabindex="-1"></a><span class="co">#bring in the loaded models </span></span>
<span id="cb216-2"><a href="#cb216-2" tabindex="-1"></a>Americas_hyp4 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Americas_hyp4.rds&quot;</span>)</span>
<span id="cb216-3"><a href="#cb216-3" tabindex="-1"></a>CSES_hyp4 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;CSES_hyp4.rds&quot;</span>)</span>
<span id="cb216-4"><a href="#cb216-4" tabindex="-1"></a>Euro_hyp4 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Euro_hyp4.rds&quot;</span>)</span>
<span id="cb216-5"><a href="#cb216-5" tabindex="-1"></a>Latino_hyp4 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Latino_hyp4.rds&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb217"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb217-1"><a href="#cb217-1" tabindex="-1"></a>Americas.corruption.emm <span class="ot">&lt;-</span> <span class="fu">emmeans</span>(Americas_hyp3a, pairwise <span class="sc">~</span> Corruption <span class="sc">|</span> quota_effect,  <span class="at">at =</span> <span class="fu">list</span>(<span class="at">Corruption =</span> <span class="fu">c</span>(<span class="fl">0.36</span>, <span class="fl">0.63</span>,<span class="fl">0.9</span>)), <span class="at">adjust =</span> <span class="st">&quot;fdr&quot;</span>)</span></code></pre></div>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;pbkrtest.limit = 107527&#39; (or larger)
## [or, globally, &#39;set emm_options(pbkrtest.limit = 107527)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;lmerTest.limit = 107527&#39; (or larger)
## [or, globally, &#39;set emm_options(lmerTest.limit = 107527)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<div class="sourceCode" id="cb220"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb220-1"><a href="#cb220-1" tabindex="-1"></a><span class="fu">summary</span>(Americas.corruption.emm)</span></code></pre></div>
<pre><code>## $emmeans
## quota_effect = No Quota:
##  Corruption emmean      SE  df asymp.LCL asymp.UCL
##        0.36  0.544 0.01047 Inf     0.523     0.564
##        0.63  0.509 0.00886 Inf     0.492     0.527
##        0.90  0.475 0.01410 Inf     0.447     0.502
## 
## quota_effect = Non-Effective:
##  Corruption emmean      SE  df asymp.LCL asymp.UCL
##        0.36  0.571 0.01609 Inf     0.539     0.602
##        0.63  0.536 0.01181 Inf     0.513     0.559
##        0.90  0.501 0.01309 Inf     0.476     0.527
## 
## quota_effect = Effective:
##  Corruption emmean      SE  df asymp.LCL asymp.UCL
##        0.36  0.527 0.01517 Inf     0.497     0.556
##        0.63  0.492 0.01097 Inf     0.470     0.513
##        0.90  0.457 0.01272 Inf     0.432     0.482
## 
## Results are averaged over the levels of: Female 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95 
## 
## $contrasts
## quota_effect = No Quota:
##  contrast                        estimate     SE  df z.ratio p.value
##  Corruption0.36 - Corruption0.63   0.0346 0.0087 Inf   3.979  0.0001
##  Corruption0.36 - Corruption0.9    0.0692 0.0174 Inf   3.979  0.0001
##  Corruption0.63 - Corruption0.9    0.0346 0.0087 Inf   3.979  0.0001
## 
## quota_effect = Non-Effective:
##  contrast                        estimate     SE  df z.ratio p.value
##  Corruption0.36 - Corruption0.63   0.0346 0.0087 Inf   3.979  0.0001
##  Corruption0.36 - Corruption0.9    0.0692 0.0174 Inf   3.979  0.0001
##  Corruption0.63 - Corruption0.9    0.0346 0.0087 Inf   3.979  0.0001
## 
## quota_effect = Effective:
##  contrast                        estimate     SE  df z.ratio p.value
##  Corruption0.36 - Corruption0.63   0.0346 0.0087 Inf   3.979  0.0001
##  Corruption0.36 - Corruption0.9    0.0692 0.0174 Inf   3.979  0.0001
##  Corruption0.63 - Corruption0.9    0.0346 0.0087 Inf   3.979  0.0001
## 
## Results are averaged over the levels of: Female 
## Degrees-of-freedom method: asymptotic 
## P value adjustment: fdr method for 3 tests</code></pre>
<div class="sourceCode" id="cb222"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb222-1"><a href="#cb222-1" tabindex="-1"></a>Corruption_americas_diffs <span class="ot">&lt;-</span> <span class="fu">pairs</span>(<span class="fu">pairs</span>(Americas.corruption.emm), <span class="at">by =</span> <span class="cn">NULL</span>, <span class="at">adjust =</span> <span class="st">&quot;fdr&quot;</span> )</span>
<span id="cb222-2"><a href="#cb222-2" tabindex="-1"></a>Corruption_americas_diffs_tab <span class="ot">&lt;-</span> <span class="fu">summary</span>(Corruption_americas_diffs)</span>
<span id="cb222-3"><a href="#cb222-3" tabindex="-1"></a></span>
<span id="cb222-4"><a href="#cb222-4" tabindex="-1"></a></span>
<span id="cb222-5"><a href="#cb222-5" tabindex="-1"></a></span>
<span id="cb222-6"><a href="#cb222-6" tabindex="-1"></a>CSES.corruption.emm <span class="ot">&lt;-</span> <span class="fu">emmeans</span>(CSES_hyp3a, pairwise <span class="sc">~</span> Corruption <span class="sc">|</span> quota_effect,  <span class="at">at =</span> <span class="fu">list</span>(<span class="at">Corruption =</span> <span class="fu">c</span>(<span class="sc">-</span><span class="fl">0.08</span>, <span class="fl">0.2</span>,<span class="fl">0.42</span>)), <span class="at">adjust =</span> <span class="st">&quot;fdr&quot;</span>)</span></code></pre></div>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;pbkrtest.limit = 156300&#39; (or larger)
## [or, globally, &#39;set emm_options(pbkrtest.limit = 156300)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;lmerTest.limit = 156300&#39; (or larger)
## [or, globally, &#39;set emm_options(lmerTest.limit = 156300)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<div class="sourceCode" id="cb225"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb225-1"><a href="#cb225-1" tabindex="-1"></a><span class="fu">summary</span>(CSES.corruption.emm)</span></code></pre></div>
<pre><code>## $emmeans
## quota_effect = No Quota:
##  Corruption emmean      SE  df asymp.LCL asymp.UCL
##       -0.08  0.592 0.01862 Inf     0.556     0.629
##        0.20  0.541 0.00824 Inf     0.525     0.557
##        0.42  0.501 0.01721 Inf     0.467     0.534
## 
## quota_effect = Non-Effective:
##  Corruption emmean      SE  df asymp.LCL asymp.UCL
##       -0.08  0.631 0.03820 Inf     0.556     0.706
##        0.20  0.579 0.03030 Inf     0.520     0.639
##        0.42  0.539 0.03066 Inf     0.479     0.599
## 
## quota_effect = Effective:
##  Corruption emmean      SE  df asymp.LCL asymp.UCL
##       -0.08  0.516 0.02928 Inf     0.459     0.573
##        0.20  0.465 0.01986 Inf     0.426     0.504
##        0.42  0.424 0.02187 Inf     0.381     0.467
## 
## Results are averaged over the levels of: Female 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95 
## 
## $contrasts
## quota_effect = No Quota:
##  contrast                           estimate     SE  df z.ratio p.value
##  (Corruption-0.08) - Corruption0.2    0.0514 0.0179 Inf   2.879  0.0040
##  (Corruption-0.08) - Corruption0.42   0.0918 0.0319 Inf   2.879  0.0040
##  Corruption0.2 - Corruption0.42       0.0404 0.0140 Inf   2.879  0.0040
## 
## quota_effect = Non-Effective:
##  contrast                           estimate     SE  df z.ratio p.value
##  (Corruption-0.08) - Corruption0.2    0.0514 0.0179 Inf   2.879  0.0040
##  (Corruption-0.08) - Corruption0.42   0.0918 0.0319 Inf   2.879  0.0040
##  Corruption0.2 - Corruption0.42       0.0404 0.0140 Inf   2.879  0.0040
## 
## quota_effect = Effective:
##  contrast                           estimate     SE  df z.ratio p.value
##  (Corruption-0.08) - Corruption0.2    0.0514 0.0179 Inf   2.879  0.0040
##  (Corruption-0.08) - Corruption0.42   0.0918 0.0319 Inf   2.879  0.0040
##  Corruption0.2 - Corruption0.42       0.0404 0.0140 Inf   2.879  0.0040
## 
## Results are averaged over the levels of: Female 
## Degrees-of-freedom method: asymptotic 
## P value adjustment: fdr method for 3 tests</code></pre>
<div class="sourceCode" id="cb227"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb227-1"><a href="#cb227-1" tabindex="-1"></a>Corruption_cses_diffs <span class="ot">&lt;-</span> <span class="fu">pairs</span>(<span class="fu">pairs</span>(CSES.corruption.emm), <span class="at">by =</span> <span class="cn">NULL</span>, <span class="at">adjust =</span> <span class="st">&quot;fdr&quot;</span> )</span>
<span id="cb227-2"><a href="#cb227-2" tabindex="-1"></a>Corruption_cses_diffs_tab <span class="ot">&lt;-</span> <span class="fu">summary</span>(Corruption_cses_diffs)</span>
<span id="cb227-3"><a href="#cb227-3" tabindex="-1"></a></span>
<span id="cb227-4"><a href="#cb227-4" tabindex="-1"></a></span>
<span id="cb227-5"><a href="#cb227-5" tabindex="-1"></a></span>
<span id="cb227-6"><a href="#cb227-6" tabindex="-1"></a>Euro.corruption.emm <span class="ot">&lt;-</span> <span class="fu">emmeans</span>(Euro_hyp3a, pairwise <span class="sc">~</span> Corruption <span class="sc">|</span> quota_effect,  <span class="at">at =</span> <span class="fu">list</span>(<span class="at">Corruption =</span> <span class="fu">c</span>(<span class="sc">-</span><span class="fl">0.02</span>, <span class="fl">0.13</span>,<span class="fl">0.27</span>)), <span class="at">adjust =</span> <span class="st">&quot;fdr&quot;</span>)</span></code></pre></div>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;pbkrtest.limit = 581321&#39; (or larger)
## [or, globally, &#39;set emm_options(pbkrtest.limit = 581321)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;lmerTest.limit = 581321&#39; (or larger)
## [or, globally, &#39;set emm_options(lmerTest.limit = 581321)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<div class="sourceCode" id="cb230"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb230-1"><a href="#cb230-1" tabindex="-1"></a><span class="fu">summary</span>(Euro.corruption.emm)</span></code></pre></div>
<pre><code>## $emmeans
## quota_effect = No Quota:
##  Corruption emmean      SE  df asymp.LCL asymp.UCL
##       -0.02  0.558 0.00671 Inf     0.545     0.571
##        0.13  0.516 0.00350 Inf     0.509     0.523
##        0.27  0.477 0.00645 Inf     0.465     0.490
## 
## quota_effect = Non-Effective:
##  Corruption emmean      SE  df asymp.LCL asymp.UCL
##       -0.02  0.548 0.02042 Inf     0.508     0.588
##        0.13  0.506 0.01977 Inf     0.467     0.545
##        0.27  0.467 0.02065 Inf     0.427     0.508
## 
## quota_effect = Effective:
##  Corruption emmean      SE  df asymp.LCL asymp.UCL
##       -0.02  0.477 0.01569 Inf     0.446     0.507
##        0.13  0.435 0.01457 Inf     0.406     0.463
##        0.27  0.396 0.01552 Inf     0.365     0.426
## 
## Results are averaged over the levels of: Female 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95 
## 
## $contrasts
## quota_effect = No Quota:
##  contrast                           estimate      SE  df z.ratio p.value
##  (Corruption-0.02) - Corruption0.13   0.0418 0.00576 Inf   7.264  &lt;.0001
##  (Corruption-0.02) - Corruption0.27   0.0809 0.01114 Inf   7.264  &lt;.0001
##  Corruption0.13 - Corruption0.27      0.0391 0.00538 Inf   7.264  &lt;.0001
## 
## quota_effect = Non-Effective:
##  contrast                           estimate      SE  df z.ratio p.value
##  (Corruption-0.02) - Corruption0.13   0.0418 0.00576 Inf   7.264  &lt;.0001
##  (Corruption-0.02) - Corruption0.27   0.0809 0.01114 Inf   7.264  &lt;.0001
##  Corruption0.13 - Corruption0.27      0.0391 0.00538 Inf   7.264  &lt;.0001
## 
## quota_effect = Effective:
##  contrast                           estimate      SE  df z.ratio p.value
##  (Corruption-0.02) - Corruption0.13   0.0418 0.00576 Inf   7.264  &lt;.0001
##  (Corruption-0.02) - Corruption0.27   0.0809 0.01114 Inf   7.264  &lt;.0001
##  Corruption0.13 - Corruption0.27      0.0391 0.00538 Inf   7.264  &lt;.0001
## 
## Results are averaged over the levels of: Female 
## Degrees-of-freedom method: asymptotic 
## P value adjustment: fdr method for 3 tests</code></pre>
<div class="sourceCode" id="cb232"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb232-1"><a href="#cb232-1" tabindex="-1"></a>Corruption_euro_diffs <span class="ot">&lt;-</span> <span class="fu">pairs</span>(<span class="fu">pairs</span>(Euro.corruption.emm), <span class="at">by =</span> <span class="cn">NULL</span>, <span class="at">adjust =</span> <span class="st">&quot;fdr&quot;</span> )</span>
<span id="cb232-2"><a href="#cb232-2" tabindex="-1"></a>Corruption_euro_diffs_tab <span class="ot">&lt;-</span> <span class="fu">summary</span>(Corruption_euro_diffs)</span>
<span id="cb232-3"><a href="#cb232-3" tabindex="-1"></a></span>
<span id="cb232-4"><a href="#cb232-4" tabindex="-1"></a></span>
<span id="cb232-5"><a href="#cb232-5" tabindex="-1"></a></span>
<span id="cb232-6"><a href="#cb232-6" tabindex="-1"></a>Latino.corruption.emm <span class="ot">&lt;-</span> <span class="fu">emmeans</span>(Latino_hyp3a, pairwise <span class="sc">~</span> Corruption <span class="sc">|</span> quota_effect,  <span class="at">at =</span> <span class="fu">list</span>(<span class="at">Corruption =</span> <span class="fu">c</span>(<span class="fl">0.25</span>, <span class="fl">0.54</span>,<span class="fl">0.84</span>)), <span class="at">adjust =</span> <span class="st">&quot;fdr&quot;</span>)</span></code></pre></div>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;pbkrtest.limit = 274148&#39; (or larger)
## [or, globally, &#39;set emm_options(pbkrtest.limit = 274148)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<pre><code>## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument &#39;lmerTest.limit = 274148&#39; (or larger)
## [or, globally, &#39;set emm_options(lmerTest.limit = 274148)&#39; or larger];
## but be warned that this may result in large computation time and memory use.</code></pre>
<div class="sourceCode" id="cb235"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb235-1"><a href="#cb235-1" tabindex="-1"></a><span class="fu">summary</span>(Latino.corruption.emm)</span></code></pre></div>
<pre><code>## $emmeans
## quota_effect = No Quota:
##  Corruption emmean      SE  df asymp.LCL asymp.UCL
##        0.25  0.570 0.01422 Inf     0.542     0.597
##        0.54  0.499 0.00979 Inf     0.480     0.518
##        0.84  0.427 0.01877 Inf     0.390     0.463
## 
## quota_effect = Non-Effective:
##  Corruption emmean      SE  df asymp.LCL asymp.UCL
##        0.25  0.542 0.02228 Inf     0.498     0.586
##        0.54  0.472 0.01504 Inf     0.442     0.501
##        0.84  0.399 0.01770 Inf     0.364     0.434
## 
## quota_effect = Effective:
##  Corruption emmean      SE  df asymp.LCL asymp.UCL
##        0.25  0.504 0.02091 Inf     0.463     0.545
##        0.54  0.433 0.01310 Inf     0.408     0.459
##        0.84  0.361 0.01623 Inf     0.329     0.392
## 
## Results are averaged over the levels of: Female 
## Degrees-of-freedom method: asymptotic 
## Confidence level used: 0.95 
## 
## $contrasts
## quota_effect = No Quota:
##  contrast                        estimate     SE  df z.ratio p.value
##  Corruption0.25 - Corruption0.54   0.0703 0.0132 Inf   5.324  &lt;.0001
##  Corruption0.25 - Corruption0.84   0.1430 0.0269 Inf   5.324  &lt;.0001
##  Corruption0.54 - Corruption0.84   0.0727 0.0137 Inf   5.324  &lt;.0001
## 
## quota_effect = Non-Effective:
##  contrast                        estimate     SE  df z.ratio p.value
##  Corruption0.25 - Corruption0.54   0.0703 0.0132 Inf   5.324  &lt;.0001
##  Corruption0.25 - Corruption0.84   0.1430 0.0269 Inf   5.324  &lt;.0001
##  Corruption0.54 - Corruption0.84   0.0727 0.0137 Inf   5.324  &lt;.0001
## 
## quota_effect = Effective:
##  contrast                        estimate     SE  df z.ratio p.value
##  Corruption0.25 - Corruption0.54   0.0703 0.0132 Inf   5.324  &lt;.0001
##  Corruption0.25 - Corruption0.84   0.1430 0.0269 Inf   5.324  &lt;.0001
##  Corruption0.54 - Corruption0.84   0.0727 0.0137 Inf   5.324  &lt;.0001
## 
## Results are averaged over the levels of: Female 
## Degrees-of-freedom method: asymptotic 
## P value adjustment: fdr method for 3 tests</code></pre>
<div class="sourceCode" id="cb237"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb237-1"><a href="#cb237-1" tabindex="-1"></a>Corruption_latino_diffs <span class="ot">&lt;-</span> <span class="fu">pairs</span>(<span class="fu">pairs</span>(Latino.corruption.emm), <span class="at">by =</span> <span class="cn">NULL</span>, <span class="at">adjust =</span> <span class="st">&quot;fdr&quot;</span> )</span>
<span id="cb237-2"><a href="#cb237-2" tabindex="-1"></a>Corruption_latino_diffs_tab <span class="ot">&lt;-</span> <span class="fu">summary</span>(Corruption_latino_diffs)</span></code></pre></div>
</div>
</div>
<div id="appendix-study-1" class="section level2">
<h2>Appendix-Study 1</h2>
<div id="quota-dotcharts" class="section level3">
<h3>Quota dotcharts</h3>
<p>Dotcharts of satisfaction levels for each country-year in the
AmericasBarometer, CSES, Eurobarometer, and Latinobarometer.</p>
<div id="americasbarometer-dotchart" class="section level4">
<h4>AmericasBarometer dotchart</h4>
<div class="sourceCode" id="cb238"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb238-1"><a href="#cb238-1" tabindex="-1"></a>Americas_analysis <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Analysis Data/Americas_analysis.rds&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb239"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb239-1"><a href="#cb239-1" tabindex="-1"></a>Americas_agg <span class="ot">&lt;-</span> Americas_analysis <span class="sc">%&gt;%</span></span>
<span id="cb239-2"><a href="#cb239-2" tabindex="-1"></a> <span class="fu">group_by</span>(Country_year, quota_effect)<span class="sc">%&gt;%</span></span>
<span id="cb239-3"><a href="#cb239-3" tabindex="-1"></a>  <span class="fu">summarize_at</span>(<span class="fu">vars</span>(<span class="at">Satisfaction =</span> Satisfaction), </span>
<span id="cb239-4"><a href="#cb239-4" tabindex="-1"></a>                                   mean, <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
<span id="cb239-5"><a href="#cb239-5" tabindex="-1"></a></span>
<span id="cb239-6"><a href="#cb239-6" tabindex="-1"></a></span>
<span id="cb239-7"><a href="#cb239-7" tabindex="-1"></a>Country_year_label <span class="ot">&lt;-</span> <span class="fu">attributes</span>(Americas_agg<span class="sc">$</span>Country_year)<span class="sc">$</span>labels</span>
<span id="cb239-8"><a href="#cb239-8" tabindex="-1"></a>Americas_agg<span class="sc">$</span>Country_year <span class="ot">&lt;-</span> <span class="fu">names</span>(Country_year_label)[<span class="fu">match</span>(Americas_agg<span class="sc">$</span>Country_year, Country_year_label)]</span>
<span id="cb239-9"><a href="#cb239-9" tabindex="-1"></a></span>
<span id="cb239-10"><a href="#cb239-10" tabindex="-1"></a></span>
<span id="cb239-11"><a href="#cb239-11" tabindex="-1"></a></span>
<span id="cb239-12"><a href="#cb239-12" tabindex="-1"></a></span>
<span id="cb239-13"><a href="#cb239-13" tabindex="-1"></a>Americas_agg <span class="ot">&lt;-</span> Americas_agg[<span class="fu">order</span>(Americas_agg<span class="sc">$</span>Country_year),]</span>
<span id="cb239-14"><a href="#cb239-14" tabindex="-1"></a></span>
<span id="cb239-15"><a href="#cb239-15" tabindex="-1"></a>Americas_agg <span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(Americas_agg[<span class="fu">order</span>(<span class="fu">rev</span>(Americas_agg<span class="sc">$</span>Country_year), <span class="at">decreasing =</span> <span class="cn">TRUE</span>), ])</span></code></pre></div>
<div class="sourceCode" id="cb240"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb240-1"><a href="#cb240-1" tabindex="-1"></a>Americas_dotplot <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(Americas_agg, <span class="fu">aes</span>(<span class="at">x =</span> Satisfaction, <span class="at">y =</span> <span class="fu">reorder</span>(Country_year, <span class="fu">desc</span>(Country_year)))) <span class="sc">+</span></span>
<span id="cb240-2"><a href="#cb240-2" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">shape=</span>quota_effect, <span class="at">colour =</span> quota_effect), <span class="at">size =</span> <span class="fl">1.3</span>, <span class="at">position=</span><span class="fu">position_dodge</span>(<span class="at">width=</span><span class="dv">0</span>)) <span class="sc">+</span> </span>
<span id="cb240-3"><a href="#cb240-3" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>)) <span class="sc">+</span></span>
<span id="cb240-4"><a href="#cb240-4" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="dv">10</span>) <span class="sc">+</span></span>
<span id="cb240-5"><a href="#cb240-5" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.y =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="fu">rel</span>(.<span class="dv">7</span>)),</span>
<span id="cb240-6"><a href="#cb240-6" tabindex="-1"></a>        <span class="at">axis.ticks.y =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb240-7"><a href="#cb240-7" tabindex="-1"></a>        <span class="at">axis.title.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="fu">rel</span>(.<span class="dv">9</span>)),</span>
<span id="cb240-8"><a href="#cb240-8" tabindex="-1"></a>        <span class="at">panel.grid.major.x =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb240-9"><a href="#cb240-9" tabindex="-1"></a>        <span class="at">panel.grid.major.y =</span> <span class="fu">element_line</span>(<span class="at">size =</span> <span class="fl">0.4</span>),</span>
<span id="cb240-10"><a href="#cb240-10" tabindex="-1"></a>        <span class="at">panel.grid.minor.x =</span> <span class="fu">element_blank</span>(), </span>
<span id="cb240-11"><a href="#cb240-11" tabindex="-1"></a>        <span class="at">legend.position =</span> <span class="st">&quot;bottom&quot;</span>, </span>
<span id="cb240-12"><a href="#cb240-12" tabindex="-1"></a>        <span class="at">legend.title =</span> <span class="fu">element_blank</span>()) <span class="sc">+</span></span>
<span id="cb240-13"><a href="#cb240-13" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;springgreen3&quot;</span>,<span class="st">&quot;steelblue1&quot;</span>,<span class="st">&quot;indianred2&quot;</span>)) <span class="sc">+</span></span>
<span id="cb240-14"><a href="#cb240-14" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Satisfaction&quot;</span>) <span class="sc">+</span></span>
<span id="cb240-15"><a href="#cb240-15" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;&quot;</span>)</span></code></pre></div>
<pre><code>## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.</code></pre>
<div class="sourceCode" id="cb242"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb242-1"><a href="#cb242-1" tabindex="-1"></a>Americas_dotplot</span></code></pre></div>
<p><img src="data:image/png;base64,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" width="672" /></p>
<div class="sourceCode" id="cb243"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb243-1"><a href="#cb243-1" tabindex="-1"></a> <span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_A1.pdf&quot;</span>,<span class="at">width =</span> <span class="fl">6.5</span>, <span class="at">height =</span> <span class="fl">12.5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
</div>
<div id="cses-dotchart" class="section level4">
<h4>CSES dotchart</h4>
<div class="sourceCode" id="cb244"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb244-1"><a href="#cb244-1" tabindex="-1"></a>CSES_analysis <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Analysis Data/CSES_analysis.rds&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb245"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb245-1"><a href="#cb245-1" tabindex="-1"></a>CSES_analysis<span class="sc">$</span>Country_Year <span class="ot">&lt;-</span> <span class="fu">as.character</span>(<span class="fu">paste</span>(CSES_analysis<span class="sc">$</span>Country, CSES_analysis<span class="sc">$</span>Year))</span>
<span id="cb245-2"><a href="#cb245-2" tabindex="-1"></a></span>
<span id="cb245-3"><a href="#cb245-3" tabindex="-1"></a></span>
<span id="cb245-4"><a href="#cb245-4" tabindex="-1"></a></span>
<span id="cb245-5"><a href="#cb245-5" tabindex="-1"></a>CSES_agg <span class="ot">&lt;-</span> CSES_analysis <span class="sc">%&gt;%</span></span>
<span id="cb245-6"><a href="#cb245-6" tabindex="-1"></a> <span class="fu">group_by</span>(Country_Year, quota_effect, Country_year)<span class="sc">%&gt;%</span></span>
<span id="cb245-7"><a href="#cb245-7" tabindex="-1"></a>  <span class="fu">summarize_at</span>(<span class="fu">vars</span>(<span class="at">Satisfaction =</span> Satisfaction), </span>
<span id="cb245-8"><a href="#cb245-8" tabindex="-1"></a>                                   mean, <span class="at">na.rm =</span> <span class="cn">FALSE</span>)</span>
<span id="cb245-9"><a href="#cb245-9" tabindex="-1"></a></span>
<span id="cb245-10"><a href="#cb245-10" tabindex="-1"></a></span>
<span id="cb245-11"><a href="#cb245-11" tabindex="-1"></a></span>
<span id="cb245-12"><a href="#cb245-12" tabindex="-1"></a></span>
<span id="cb245-13"><a href="#cb245-13" tabindex="-1"></a>CSES_agg <span class="ot">&lt;-</span> CSES_agg[<span class="fu">order</span>(CSES_agg<span class="sc">$</span>Country_Year),]</span>
<span id="cb245-14"><a href="#cb245-14" tabindex="-1"></a></span>
<span id="cb245-15"><a href="#cb245-15" tabindex="-1"></a>CSES_agg <span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(CSES_agg[<span class="fu">order</span>(<span class="fu">rev</span>(CSES_agg<span class="sc">$</span>Country_Year), <span class="at">decreasing =</span> <span class="cn">TRUE</span>), ])</span>
<span id="cb245-16"><a href="#cb245-16" tabindex="-1"></a></span>
<span id="cb245-17"><a href="#cb245-17" tabindex="-1"></a></span>
<span id="cb245-18"><a href="#cb245-18" tabindex="-1"></a></span>
<span id="cb245-19"><a href="#cb245-19" tabindex="-1"></a>CSES_agg1 <span class="ot">&lt;-</span> CSES_agg[<span class="dv">1</span><span class="sc">:</span><span class="dv">72</span>,]</span>
<span id="cb245-20"><a href="#cb245-20" tabindex="-1"></a>CSES_agg2 <span class="ot">&lt;-</span> CSES_agg[<span class="dv">73</span><span class="sc">:</span><span class="dv">144</span>,]</span></code></pre></div>
<div class="sourceCode" id="cb246"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb246-1"><a href="#cb246-1" tabindex="-1"></a>theme_dotplot <span class="ot">&lt;-</span> <span class="fu">theme_bw</span>(<span class="dv">10</span>) <span class="sc">+</span></span>
<span id="cb246-2"><a href="#cb246-2" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.y =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="fu">rel</span>(.<span class="dv">7</span>)),</span>
<span id="cb246-3"><a href="#cb246-3" tabindex="-1"></a>        <span class="at">axis.ticks.y =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb246-4"><a href="#cb246-4" tabindex="-1"></a>        <span class="at">axis.title.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="fu">rel</span>(.<span class="dv">9</span>)),</span>
<span id="cb246-5"><a href="#cb246-5" tabindex="-1"></a>        <span class="at">panel.grid.major.x =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb246-6"><a href="#cb246-6" tabindex="-1"></a>        <span class="at">panel.grid.major.y =</span> <span class="fu">element_line</span>(<span class="at">size =</span> <span class="fl">0.4</span>),</span>
<span id="cb246-7"><a href="#cb246-7" tabindex="-1"></a>        <span class="at">panel.grid.minor.x =</span> <span class="fu">element_blank</span>(), </span>
<span id="cb246-8"><a href="#cb246-8" tabindex="-1"></a>        <span class="at">legend.position =</span> <span class="st">&quot;none&quot;</span>)</span>
<span id="cb246-9"><a href="#cb246-9" tabindex="-1"></a></span>
<span id="cb246-10"><a href="#cb246-10" tabindex="-1"></a></span>
<span id="cb246-11"><a href="#cb246-11" tabindex="-1"></a></span>
<span id="cb246-12"><a href="#cb246-12" tabindex="-1"></a></span>
<span id="cb246-13"><a href="#cb246-13" tabindex="-1"></a>CSES_agg1 <span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(CSES_agg1[<span class="fu">order</span>(<span class="fu">rev</span>(CSES_agg1<span class="sc">$</span>Country_year), <span class="at">decreasing =</span> <span class="cn">TRUE</span>), ])</span>
<span id="cb246-14"><a href="#cb246-14" tabindex="-1"></a></span>
<span id="cb246-15"><a href="#cb246-15" tabindex="-1"></a>CSES_agg2 <span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(CSES_agg2[<span class="fu">order</span>(<span class="fu">rev</span>(CSES_agg2<span class="sc">$</span>Country_year), <span class="at">decreasing =</span> <span class="cn">TRUE</span>), ])</span></code></pre></div>
<div class="sourceCode" id="cb247"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb247-1"><a href="#cb247-1" tabindex="-1"></a>plot1 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(CSES_agg1, <span class="fu">aes</span>(<span class="at">x =</span> Satisfaction, <span class="at">y =</span> <span class="fu">reorder</span>(Country_Year, <span class="fu">desc</span>(Country_Year)))) <span class="sc">+</span></span>
<span id="cb247-2"><a href="#cb247-2" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">shape=</span>quota_effect, <span class="at">colour =</span> quota_effect), <span class="at">size =</span> <span class="fl">1.3</span>, <span class="at">position=</span><span class="fu">position_dodge</span>(<span class="at">width=</span><span class="dv">0</span>)) <span class="sc">+</span> </span>
<span id="cb247-3"><a href="#cb247-3" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>)) <span class="sc">+</span></span>
<span id="cb247-4"><a href="#cb247-4" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="dv">10</span>) <span class="sc">+</span></span>
<span id="cb247-5"><a href="#cb247-5" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.y =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="fu">rel</span>(.<span class="dv">7</span>)),</span>
<span id="cb247-6"><a href="#cb247-6" tabindex="-1"></a>        <span class="at">axis.ticks.y =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb247-7"><a href="#cb247-7" tabindex="-1"></a>        <span class="at">axis.title.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="fu">rel</span>(.<span class="dv">9</span>)),</span>
<span id="cb247-8"><a href="#cb247-8" tabindex="-1"></a>        <span class="at">panel.grid.major.x =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb247-9"><a href="#cb247-9" tabindex="-1"></a>        <span class="at">panel.grid.major.y =</span> <span class="fu">element_line</span>(<span class="at">size =</span> <span class="fl">0.4</span>),</span>
<span id="cb247-10"><a href="#cb247-10" tabindex="-1"></a>        <span class="at">panel.grid.minor.x =</span> <span class="fu">element_blank</span>(), </span>
<span id="cb247-11"><a href="#cb247-11" tabindex="-1"></a>        <span class="at">legend.position =</span> <span class="st">&quot;bottom&quot;</span>, </span>
<span id="cb247-12"><a href="#cb247-12" tabindex="-1"></a>        <span class="at">legend.title =</span> <span class="fu">element_blank</span>()) <span class="sc">+</span></span>
<span id="cb247-13"><a href="#cb247-13" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;springgreen3&quot;</span>,<span class="st">&quot;steelblue1&quot;</span>,<span class="st">&quot;indianred2&quot;</span>)) <span class="sc">+</span></span>
<span id="cb247-14"><a href="#cb247-14" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Satisfaction&quot;</span>) <span class="sc">+</span></span>
<span id="cb247-15"><a href="#cb247-15" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb248"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb248-1"><a href="#cb248-1" tabindex="-1"></a>plot2 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(CSES_agg2, <span class="fu">aes</span>(<span class="at">x =</span> Satisfaction, <span class="at">y =</span> <span class="fu">reorder</span>(Country_Year, <span class="fu">desc</span>(Country_Year)))) <span class="sc">+</span></span>
<span id="cb248-2"><a href="#cb248-2" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">shape=</span>quota_effect, <span class="at">colour =</span> quota_effect), <span class="at">size =</span> <span class="fl">1.3</span>, <span class="at">position=</span><span class="fu">position_dodge</span>(<span class="at">width=</span><span class="dv">0</span>)) <span class="sc">+</span> </span>
<span id="cb248-3"><a href="#cb248-3" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>)) <span class="sc">+</span></span>
<span id="cb248-4"><a href="#cb248-4" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="dv">10</span>) <span class="sc">+</span></span>
<span id="cb248-5"><a href="#cb248-5" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.y =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="fu">rel</span>(.<span class="dv">65</span>)),</span>
<span id="cb248-6"><a href="#cb248-6" tabindex="-1"></a>        <span class="at">axis.ticks.y =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb248-7"><a href="#cb248-7" tabindex="-1"></a>        <span class="at">axis.title.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="fu">rel</span>(.<span class="dv">9</span>)),</span>
<span id="cb248-8"><a href="#cb248-8" tabindex="-1"></a>        <span class="at">panel.grid.major.x =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb248-9"><a href="#cb248-9" tabindex="-1"></a>        <span class="at">panel.grid.major.y =</span> <span class="fu">element_line</span>(<span class="at">size =</span> <span class="fl">0.4</span>),</span>
<span id="cb248-10"><a href="#cb248-10" tabindex="-1"></a>        <span class="at">panel.grid.minor.x =</span> <span class="fu">element_blank</span>(), </span>
<span id="cb248-11"><a href="#cb248-11" tabindex="-1"></a>        <span class="at">legend.position =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span></span>
<span id="cb248-12"><a href="#cb248-12" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;springgreen3&quot;</span>,<span class="st">&quot;steelblue1&quot;</span>,<span class="st">&quot;indianred2&quot;</span>)) <span class="sc">+</span></span>
<span id="cb248-13"><a href="#cb248-13" tabindex="-1"></a><span class="fu">xlab</span>(<span class="st">&quot;Satisfaction&quot;</span>) <span class="sc">+</span></span>
<span id="cb248-14"><a href="#cb248-14" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;&quot;</span>)</span>
<span id="cb248-15"><a href="#cb248-15" tabindex="-1"></a></span>
<span id="cb248-16"><a href="#cb248-16" tabindex="-1"></a></span>
<span id="cb248-17"><a href="#cb248-17" tabindex="-1"></a></span>
<span id="cb248-18"><a href="#cb248-18" tabindex="-1"></a><span class="fu">ggarrange</span>(plot1, plot2, <span class="at">ncol=</span><span class="dv">2</span>, <span class="at">nrow=</span><span class="dv">1</span>, <span class="at">common.legend =</span> <span class="cn">TRUE</span>, <span class="at">legend=</span><span class="st">&quot;bottom&quot;</span>) <span class="co">#5.5x7 inches</span></span></code></pre></div>
<p><img src="data:image/png;base64,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" width="672" /></p>
<div class="sourceCode" id="cb249"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb249-1"><a href="#cb249-1" tabindex="-1"></a>CSES_dotchart <span class="ot">&lt;-</span> <span class="fu">ggarrange</span>(plot1, plot2, <span class="at">ncol=</span><span class="dv">2</span>, <span class="at">nrow=</span><span class="dv">1</span>, <span class="at">common.legend =</span> <span class="cn">TRUE</span>, <span class="at">legend=</span><span class="st">&quot;bottom&quot;</span>) <span class="co">#5.5x7 inches</span></span>
<span id="cb249-2"><a href="#cb249-2" tabindex="-1"></a></span>
<span id="cb249-3"><a href="#cb249-3" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_A2.pdf&quot;</span>, CSES_dotchart, <span class="at">width =</span> <span class="fl">6.5</span>, <span class="at">height =</span> <span class="fl">11.5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
</div>
<div id="eurobarometer-dotchart" class="section level4">
<h4>Eurobarometer dotchart</h4>
<div class="sourceCode" id="cb250"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb250-1"><a href="#cb250-1" tabindex="-1"></a>Euro_analysis <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Analysis Data/Euro_analysis.rds&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb251"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb251-1"><a href="#cb251-1" tabindex="-1"></a>Euro_analysis <span class="ot">&lt;-</span> Euro_analysis <span class="sc">%&gt;%</span></span>
<span id="cb251-2"><a href="#cb251-2" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">quota_effect =</span> <span class="fu">case_when</span>(</span>
<span id="cb251-3"><a href="#cb251-3" tabindex="-1"></a>                           quota_effect <span class="sc">==</span> <span class="st">&quot;No Quota&quot;</span> <span class="sc">~</span> <span class="st">&quot;No quota&quot;</span>,</span>
<span id="cb251-4"><a href="#cb251-4" tabindex="-1"></a>                           quota_effect <span class="sc">==</span> <span class="st">&quot;Non-Effective&quot;</span> <span class="sc">~</span> <span class="st">&quot;Non-effective quota&quot;</span>,</span>
<span id="cb251-5"><a href="#cb251-5" tabindex="-1"></a>                           quota_effect <span class="sc">==</span> <span class="st">&quot;Effective&quot;</span> <span class="sc">~</span> <span class="st">&quot;Effective quota&quot;</span>))</span>
<span id="cb251-6"><a href="#cb251-6" tabindex="-1"></a>                </span>
<span id="cb251-7"><a href="#cb251-7" tabindex="-1"></a>                </span>
<span id="cb251-8"><a href="#cb251-8" tabindex="-1"></a>Euro_agg <span class="ot">&lt;-</span> Euro_analysis <span class="sc">%&gt;%</span></span>
<span id="cb251-9"><a href="#cb251-9" tabindex="-1"></a> <span class="fu">group_by</span>(Country_year, quota_effect)<span class="sc">%&gt;%</span></span>
<span id="cb251-10"><a href="#cb251-10" tabindex="-1"></a>  <span class="fu">summarize_at</span>(<span class="fu">vars</span>(<span class="at">Satisfaction =</span> Satisfaction), </span>
<span id="cb251-11"><a href="#cb251-11" tabindex="-1"></a>                                   mean, <span class="at">na.rm =</span> <span class="cn">FALSE</span>)</span>
<span id="cb251-12"><a href="#cb251-12" tabindex="-1"></a></span>
<span id="cb251-13"><a href="#cb251-13" tabindex="-1"></a></span>
<span id="cb251-14"><a href="#cb251-14" tabindex="-1"></a></span>
<span id="cb251-15"><a href="#cb251-15" tabindex="-1"></a>Euro_agg<span class="sc">$</span>quota_effect <span class="ot">&lt;-</span> <span class="fu">factor</span>(Euro_agg<span class="sc">$</span>quota_effect , <span class="at">levels =</span> <span class="fu">c</span>(<span class="st">&quot;No quota&quot;</span>, <span class="st">&quot;Non-effective quota&quot;</span>, <span class="st">&quot;Effective quota&quot;</span>))</span>
<span id="cb251-16"><a href="#cb251-16" tabindex="-1"></a></span>
<span id="cb251-17"><a href="#cb251-17" tabindex="-1"></a>Euro_agg <span class="ot">&lt;-</span> Euro_agg[<span class="fu">order</span>(Euro_agg<span class="sc">$</span>Country_year),]</span>
<span id="cb251-18"><a href="#cb251-18" tabindex="-1"></a></span>
<span id="cb251-19"><a href="#cb251-19" tabindex="-1"></a></span>
<span id="cb251-20"><a href="#cb251-20" tabindex="-1"></a>Euro_agg1 <span class="ot">&lt;-</span> Euro_agg[<span class="dv">1</span><span class="sc">:</span><span class="dv">86</span>,]</span>
<span id="cb251-21"><a href="#cb251-21" tabindex="-1"></a>Euro_agg2 <span class="ot">&lt;-</span> Euro_agg[<span class="dv">87</span><span class="sc">:</span><span class="dv">174</span>,]</span>
<span id="cb251-22"><a href="#cb251-22" tabindex="-1"></a>Euro_agg3 <span class="ot">&lt;-</span> Euro_agg[<span class="dv">175</span><span class="sc">:</span><span class="dv">262</span>,]</span>
<span id="cb251-23"><a href="#cb251-23" tabindex="-1"></a>Euro_agg4 <span class="ot">&lt;-</span> Euro_agg[<span class="dv">263</span><span class="sc">:</span><span class="dv">350</span>,]</span>
<span id="cb251-24"><a href="#cb251-24" tabindex="-1"></a>Euro_agg5 <span class="ot">&lt;-</span> Euro_agg[<span class="dv">351</span><span class="sc">:</span><span class="dv">438</span>,]</span>
<span id="cb251-25"><a href="#cb251-25" tabindex="-1"></a>Euro_agg6 <span class="ot">&lt;-</span> Euro_agg[<span class="dv">439</span><span class="sc">:</span><span class="dv">521</span>,]</span></code></pre></div>
<div class="sourceCode" id="cb252"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb252-1"><a href="#cb252-1" tabindex="-1"></a>Euro_agg1 <span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(Euro_agg1[<span class="fu">order</span>(<span class="fu">rev</span>(Euro_agg1<span class="sc">$</span>Country_year), <span class="at">decreasing =</span> <span class="cn">TRUE</span>), ])</span>
<span id="cb252-2"><a href="#cb252-2" tabindex="-1"></a></span>
<span id="cb252-3"><a href="#cb252-3" tabindex="-1"></a>Euro_plot_1 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(Euro_agg1, <span class="fu">aes</span>(<span class="at">x =</span> Satisfaction, <span class="at">y =</span> <span class="fu">reorder</span>(Country_year, <span class="fu">desc</span>(Country_year)))) <span class="sc">+</span></span>
<span id="cb252-4"><a href="#cb252-4" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">shape=</span>quota_effect, <span class="at">colour =</span> quota_effect), <span class="at">size =</span> <span class="fl">1.3</span>, <span class="at">position=</span><span class="fu">position_dodge</span>(<span class="at">width=</span><span class="dv">0</span>)) <span class="sc">+</span> </span>
<span id="cb252-5"><a href="#cb252-5" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>)) <span class="sc">+</span></span>
<span id="cb252-6"><a href="#cb252-6" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="dv">12</span>) <span class="sc">+</span></span>
<span id="cb252-7"><a href="#cb252-7" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.y =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="fu">rel</span>(.<span class="dv">7</span>)),</span>
<span id="cb252-8"><a href="#cb252-8" tabindex="-1"></a>        <span class="at">axis.ticks.y =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb252-9"><a href="#cb252-9" tabindex="-1"></a>        <span class="at">axis.title.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="fu">rel</span>(<span class="fl">1.1</span>)),</span>
<span id="cb252-10"><a href="#cb252-10" tabindex="-1"></a>        <span class="at">panel.grid.major.x =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb252-11"><a href="#cb252-11" tabindex="-1"></a>        <span class="at">panel.grid.major.y =</span> <span class="fu">element_line</span>(<span class="at">size =</span> <span class="fl">0.4</span>),</span>
<span id="cb252-12"><a href="#cb252-12" tabindex="-1"></a>        <span class="at">panel.grid.minor.x =</span> <span class="fu">element_blank</span>(), </span>
<span id="cb252-13"><a href="#cb252-13" tabindex="-1"></a>        <span class="at">legend.position =</span> <span class="st">&quot;none&quot;</span>, </span>
<span id="cb252-14"><a href="#cb252-14" tabindex="-1"></a>        <span class="at">legend.title =</span> <span class="fu">element_blank</span>()) <span class="sc">+</span></span>
<span id="cb252-15"><a href="#cb252-15" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;No quota&quot;</span> <span class="ot">=</span> <span class="st">&quot;springgreen3&quot;</span>,<span class="st">&quot;Non-effective quota&quot;</span> <span class="ot">=</span> <span class="st">&quot;steelblue1&quot;</span> ,<span class="st">&quot;Effective quota&quot;</span><span class="ot">=</span><span class="st">&quot;indianred2&quot;</span>), <span class="at">drop =</span> <span class="cn">FALSE</span>) <span class="sc">+</span> </span>
<span id="cb252-16"><a href="#cb252-16" tabindex="-1"></a>  <span class="fu">scale_shape_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;No quota&quot;</span> <span class="ot">=</span> <span class="dv">16</span>,<span class="st">&quot;Non-effective quota&quot;</span> <span class="ot">=</span> <span class="dv">17</span>, <span class="st">&quot;Effective quota&quot;</span> <span class="ot">=</span> <span class="dv">15</span>), <span class="at">drop =</span> <span class="cn">FALSE</span>)<span class="sc">+</span></span>
<span id="cb252-17"><a href="#cb252-17" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Satisfaction&quot;</span>) <span class="sc">+</span></span>
<span id="cb252-18"><a href="#cb252-18" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb253"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb253-1"><a href="#cb253-1" tabindex="-1"></a>Euro_agg2 <span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(Euro_agg2[<span class="fu">order</span>(<span class="fu">rev</span>(Euro_agg2<span class="sc">$</span>Country_year), <span class="at">decreasing =</span> <span class="cn">TRUE</span>), ])</span>
<span id="cb253-2"><a href="#cb253-2" tabindex="-1"></a></span>
<span id="cb253-3"><a href="#cb253-3" tabindex="-1"></a>Euro_plot_2 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(Euro_agg2, <span class="fu">aes</span>(<span class="at">x =</span> Satisfaction, <span class="at">y =</span> <span class="fu">reorder</span>(Country_year, <span class="fu">desc</span>(Country_year)))) <span class="sc">+</span></span>
<span id="cb253-4"><a href="#cb253-4" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">shape=</span>quota_effect, <span class="at">colour =</span> quota_effect), <span class="at">size =</span> <span class="fl">1.3</span>, <span class="at">position=</span><span class="fu">position_dodge</span>(<span class="at">width=</span><span class="dv">0</span>)) <span class="sc">+</span> </span>
<span id="cb253-5"><a href="#cb253-5" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>)) <span class="sc">+</span></span>
<span id="cb253-6"><a href="#cb253-6" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="dv">12</span>) <span class="sc">+</span></span>
<span id="cb253-7"><a href="#cb253-7" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.y =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="fu">rel</span>(.<span class="dv">65</span>)),</span>
<span id="cb253-8"><a href="#cb253-8" tabindex="-1"></a>        <span class="at">axis.ticks.y =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb253-9"><a href="#cb253-9" tabindex="-1"></a>        <span class="at">axis.title.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="fu">rel</span>(<span class="fl">1.1</span>)),</span>
<span id="cb253-10"><a href="#cb253-10" tabindex="-1"></a>        <span class="at">panel.grid.major.x =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb253-11"><a href="#cb253-11" tabindex="-1"></a>        <span class="at">panel.grid.major.y =</span> <span class="fu">element_line</span>(<span class="at">size =</span> <span class="fl">0.4</span>),</span>
<span id="cb253-12"><a href="#cb253-12" tabindex="-1"></a>        <span class="at">panel.grid.minor.x =</span> <span class="fu">element_blank</span>(), </span>
<span id="cb253-13"><a href="#cb253-13" tabindex="-1"></a>        <span class="at">legend.position =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span></span>
<span id="cb253-14"><a href="#cb253-14" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;No quota&quot;</span> <span class="ot">=</span> <span class="st">&quot;springgreen3&quot;</span>,<span class="st">&quot;Non-effective quota&quot;</span> <span class="ot">=</span> <span class="st">&quot;steelblue1&quot;</span> ,<span class="st">&quot;Effective quota&quot;</span><span class="ot">=</span><span class="st">&quot;indianred2&quot;</span>), <span class="at">drop =</span> <span class="cn">FALSE</span>) <span class="sc">+</span> </span>
<span id="cb253-15"><a href="#cb253-15" tabindex="-1"></a>  <span class="fu">scale_shape_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;No quota&quot;</span> <span class="ot">=</span> <span class="dv">16</span>,<span class="st">&quot;Non-effective quota&quot;</span> <span class="ot">=</span> <span class="dv">17</span>, <span class="st">&quot;Effective quota&quot;</span> <span class="ot">=</span> <span class="dv">15</span>), <span class="at">drop =</span> <span class="cn">FALSE</span>)<span class="sc">+</span></span>
<span id="cb253-16"><a href="#cb253-16" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Satisfaction&quot;</span>) <span class="sc">+</span></span>
<span id="cb253-17"><a href="#cb253-17" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb254"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb254-1"><a href="#cb254-1" tabindex="-1"></a>Euro_agg3 <span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(Euro_agg3[<span class="fu">order</span>(<span class="fu">rev</span>(Euro_agg3<span class="sc">$</span>Country_year), <span class="at">decreasing =</span> <span class="cn">TRUE</span>), ])</span>
<span id="cb254-2"><a href="#cb254-2" tabindex="-1"></a></span>
<span id="cb254-3"><a href="#cb254-3" tabindex="-1"></a></span>
<span id="cb254-4"><a href="#cb254-4" tabindex="-1"></a>Euro_plot_3 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(Euro_agg3, <span class="fu">aes</span>(<span class="at">x =</span> Satisfaction, <span class="at">y =</span> <span class="fu">reorder</span>(Country_year, <span class="fu">desc</span>(Country_year)))) <span class="sc">+</span></span>
<span id="cb254-5"><a href="#cb254-5" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">shape=</span>quota_effect, <span class="at">colour =</span> quota_effect), <span class="at">size =</span> <span class="fl">1.3</span>, <span class="at">position=</span><span class="fu">position_dodge</span>(<span class="at">width=</span><span class="dv">0</span>)) <span class="sc">+</span> </span>
<span id="cb254-6"><a href="#cb254-6" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>)) <span class="sc">+</span></span>
<span id="cb254-7"><a href="#cb254-7" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="dv">12</span>) <span class="sc">+</span></span>
<span id="cb254-8"><a href="#cb254-8" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.y =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="fu">rel</span>(.<span class="dv">65</span>)),</span>
<span id="cb254-9"><a href="#cb254-9" tabindex="-1"></a>        <span class="at">axis.ticks.y =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb254-10"><a href="#cb254-10" tabindex="-1"></a>        <span class="at">axis.title.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="fu">rel</span>(<span class="fl">1.1</span>)),</span>
<span id="cb254-11"><a href="#cb254-11" tabindex="-1"></a>        <span class="at">panel.grid.major.x =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb254-12"><a href="#cb254-12" tabindex="-1"></a>        <span class="at">panel.grid.major.y =</span> <span class="fu">element_line</span>(<span class="at">size =</span> <span class="fl">0.4</span>),</span>
<span id="cb254-13"><a href="#cb254-13" tabindex="-1"></a>        <span class="at">panel.grid.minor.x =</span> <span class="fu">element_blank</span>(), </span>
<span id="cb254-14"><a href="#cb254-14" tabindex="-1"></a>        <span class="at">legend.position =</span> <span class="st">&quot;bottom&quot;</span>, </span>
<span id="cb254-15"><a href="#cb254-15" tabindex="-1"></a>        <span class="at">legend.text=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">16</span>),</span>
<span id="cb254-16"><a href="#cb254-16" tabindex="-1"></a>        <span class="at">legend.title =</span> <span class="fu">element_blank</span>()) <span class="sc">+</span></span>
<span id="cb254-17"><a href="#cb254-17" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;No quota&quot;</span> <span class="ot">=</span> <span class="st">&quot;springgreen3&quot;</span>,<span class="st">&quot;Non-effective quota&quot;</span> <span class="ot">=</span> <span class="st">&quot;steelblue1&quot;</span> ,<span class="st">&quot;Effective quota&quot;</span><span class="ot">=</span><span class="st">&quot;indianred2&quot;</span>), <span class="at">drop =</span> <span class="cn">FALSE</span>) <span class="sc">+</span> </span>
<span id="cb254-18"><a href="#cb254-18" tabindex="-1"></a>  <span class="fu">scale_shape_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;No quota&quot;</span> <span class="ot">=</span> <span class="dv">16</span>,<span class="st">&quot;Non-effective quota&quot;</span> <span class="ot">=</span> <span class="dv">17</span>, <span class="st">&quot;Effective quota&quot;</span> <span class="ot">=</span> <span class="dv">15</span>), <span class="at">drop =</span> <span class="cn">FALSE</span>)<span class="sc">+</span></span>
<span id="cb254-19"><a href="#cb254-19" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Satisfaction&quot;</span>) <span class="sc">+</span></span>
<span id="cb254-20"><a href="#cb254-20" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb255"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb255-1"><a href="#cb255-1" tabindex="-1"></a>Euro_agg4 <span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(Euro_agg4[<span class="fu">order</span>(<span class="fu">rev</span>(Euro_agg4<span class="sc">$</span>Country_year), <span class="at">decreasing =</span> <span class="cn">TRUE</span>), ])</span>
<span id="cb255-2"><a href="#cb255-2" tabindex="-1"></a></span>
<span id="cb255-3"><a href="#cb255-3" tabindex="-1"></a>Euro_plot_4 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(Euro_agg4, <span class="fu">aes</span>(<span class="at">x =</span> Satisfaction, <span class="at">y =</span> <span class="fu">reorder</span>(Country_year, <span class="fu">desc</span>(Country_year)))) <span class="sc">+</span></span>
<span id="cb255-4"><a href="#cb255-4" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">shape=</span>quota_effect, <span class="at">colour =</span> quota_effect), <span class="at">size =</span> <span class="fl">1.3</span>, <span class="at">position=</span><span class="fu">position_dodge</span>(<span class="at">width=</span><span class="dv">0</span>)) <span class="sc">+</span> </span>
<span id="cb255-5"><a href="#cb255-5" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>)) <span class="sc">+</span></span>
<span id="cb255-6"><a href="#cb255-6" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="dv">12</span>) <span class="sc">+</span></span>
<span id="cb255-7"><a href="#cb255-7" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.y =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="fu">rel</span>(.<span class="dv">65</span>)),</span>
<span id="cb255-8"><a href="#cb255-8" tabindex="-1"></a>        <span class="at">axis.ticks.y =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb255-9"><a href="#cb255-9" tabindex="-1"></a>        <span class="at">axis.title.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="fu">rel</span>(<span class="fl">1.1</span>)),</span>
<span id="cb255-10"><a href="#cb255-10" tabindex="-1"></a>        <span class="at">panel.grid.major.x =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb255-11"><a href="#cb255-11" tabindex="-1"></a>        <span class="at">panel.grid.major.y =</span> <span class="fu">element_line</span>(<span class="at">size =</span> <span class="fl">0.4</span>),</span>
<span id="cb255-12"><a href="#cb255-12" tabindex="-1"></a>        <span class="at">panel.grid.minor.x =</span> <span class="fu">element_blank</span>(), </span>
<span id="cb255-13"><a href="#cb255-13" tabindex="-1"></a>        <span class="at">legend.position =</span> <span class="st">&quot;none&quot;</span>, </span>
<span id="cb255-14"><a href="#cb255-14" tabindex="-1"></a>        <span class="at">legend.title =</span> <span class="fu">element_blank</span>()) <span class="sc">+</span></span>
<span id="cb255-15"><a href="#cb255-15" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;No quota&quot;</span> <span class="ot">=</span> <span class="st">&quot;springgreen3&quot;</span>,<span class="st">&quot;Non-effective quota&quot;</span> <span class="ot">=</span> <span class="st">&quot;steelblue1&quot;</span> ,<span class="st">&quot;Effective quota&quot;</span><span class="ot">=</span><span class="st">&quot;indianred2&quot;</span>), <span class="at">drop =</span> <span class="cn">FALSE</span>) <span class="sc">+</span>  </span>
<span id="cb255-16"><a href="#cb255-16" tabindex="-1"></a>  <span class="fu">scale_shape_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;No quota&quot;</span> <span class="ot">=</span> <span class="dv">16</span>,<span class="st">&quot;Non-effective quota&quot;</span> <span class="ot">=</span> <span class="dv">17</span>, <span class="st">&quot;Effective quota&quot;</span> <span class="ot">=</span> <span class="dv">15</span>), <span class="at">drop =</span> <span class="cn">FALSE</span>)<span class="sc">+</span></span>
<span id="cb255-17"><a href="#cb255-17" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Satisfaction&quot;</span>) <span class="sc">+</span></span>
<span id="cb255-18"><a href="#cb255-18" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb256"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb256-1"><a href="#cb256-1" tabindex="-1"></a>Euro_agg5 <span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(Euro_agg5[<span class="fu">order</span>(<span class="fu">rev</span>(Euro_agg5<span class="sc">$</span>Country_year), <span class="at">decreasing =</span> <span class="cn">TRUE</span>), ])</span>
<span id="cb256-2"><a href="#cb256-2" tabindex="-1"></a></span>
<span id="cb256-3"><a href="#cb256-3" tabindex="-1"></a>Euro_plot_5 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(Euro_agg5, <span class="fu">aes</span>(<span class="at">x =</span> Satisfaction, <span class="at">y =</span> <span class="fu">reorder</span>(Country_year, <span class="fu">desc</span>(Country_year)))) <span class="sc">+</span></span>
<span id="cb256-4"><a href="#cb256-4" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">shape=</span>quota_effect, <span class="at">colour =</span> quota_effect), <span class="at">size =</span> <span class="fl">1.3</span>, <span class="at">position=</span><span class="fu">position_dodge</span>(<span class="at">width=</span><span class="dv">0</span>)) <span class="sc">+</span> </span>
<span id="cb256-5"><a href="#cb256-5" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>)) <span class="sc">+</span></span>
<span id="cb256-6"><a href="#cb256-6" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="dv">12</span>) <span class="sc">+</span></span>
<span id="cb256-7"><a href="#cb256-7" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.y =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="fu">rel</span>(.<span class="dv">65</span>)),</span>
<span id="cb256-8"><a href="#cb256-8" tabindex="-1"></a>        <span class="at">axis.ticks.y =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb256-9"><a href="#cb256-9" tabindex="-1"></a>        <span class="at">axis.title.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="fu">rel</span>(.<span class="dv">9</span>)),</span>
<span id="cb256-10"><a href="#cb256-10" tabindex="-1"></a>        <span class="at">panel.grid.major.x =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb256-11"><a href="#cb256-11" tabindex="-1"></a>        <span class="at">panel.grid.major.y =</span> <span class="fu">element_line</span>(<span class="at">size =</span> <span class="fl">0.4</span>),</span>
<span id="cb256-12"><a href="#cb256-12" tabindex="-1"></a>        <span class="at">panel.grid.minor.x =</span> <span class="fu">element_blank</span>(), </span>
<span id="cb256-13"><a href="#cb256-13" tabindex="-1"></a>        <span class="at">legend.position =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span></span>
<span id="cb256-14"><a href="#cb256-14" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;No quota&quot;</span> <span class="ot">=</span> <span class="st">&quot;springgreen3&quot;</span>,<span class="st">&quot;Non-effective quota&quot;</span> <span class="ot">=</span> <span class="st">&quot;steelblue1&quot;</span> ,<span class="st">&quot;Effective quota&quot;</span><span class="ot">=</span><span class="st">&quot;indianred2&quot;</span>), <span class="at">drop =</span> <span class="cn">FALSE</span>) <span class="sc">+</span> </span>
<span id="cb256-15"><a href="#cb256-15" tabindex="-1"></a>  <span class="fu">scale_shape_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;No quota&quot;</span> <span class="ot">=</span> <span class="dv">16</span>,<span class="st">&quot;Non-effective quota&quot;</span> <span class="ot">=</span> <span class="dv">17</span>, <span class="st">&quot;Effective quota&quot;</span> <span class="ot">=</span> <span class="dv">15</span>, <span class="at">drop =</span> <span class="cn">FALSE</span>))<span class="sc">+</span></span>
<span id="cb256-16"><a href="#cb256-16" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Satisfaction&quot;</span>) <span class="sc">+</span></span>
<span id="cb256-17"><a href="#cb256-17" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb257"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb257-1"><a href="#cb257-1" tabindex="-1"></a>Euro_agg6 <span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(Euro_agg6[<span class="fu">order</span>(<span class="fu">rev</span>(Euro_agg6<span class="sc">$</span>Country_year), <span class="at">decreasing =</span> <span class="cn">TRUE</span>), ])</span>
<span id="cb257-2"><a href="#cb257-2" tabindex="-1"></a></span>
<span id="cb257-3"><a href="#cb257-3" tabindex="-1"></a>Euro_plot_6 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(Euro_agg6, <span class="fu">aes</span>(<span class="at">x =</span> Satisfaction, <span class="at">y =</span> <span class="fu">reorder</span>(Country_year, <span class="fu">desc</span>(Country_year)))) <span class="sc">+</span></span>
<span id="cb257-4"><a href="#cb257-4" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">shape=</span>quota_effect, <span class="at">colour =</span> quota_effect), <span class="at">size =</span> <span class="fl">1.3</span>, <span class="at">position=</span><span class="fu">position_dodge</span>(<span class="at">width=</span><span class="dv">0</span>)) <span class="sc">+</span> </span>
<span id="cb257-5"><a href="#cb257-5" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>)) <span class="sc">+</span></span>
<span id="cb257-6"><a href="#cb257-6" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="dv">12</span>) <span class="sc">+</span></span>
<span id="cb257-7"><a href="#cb257-7" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.y =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="fu">rel</span>(.<span class="dv">65</span>)),</span>
<span id="cb257-8"><a href="#cb257-8" tabindex="-1"></a>        <span class="at">axis.ticks.y =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb257-9"><a href="#cb257-9" tabindex="-1"></a>        <span class="at">axis.title.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="fu">rel</span>(.<span class="dv">9</span>)),</span>
<span id="cb257-10"><a href="#cb257-10" tabindex="-1"></a>        <span class="at">panel.grid.major.x =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb257-11"><a href="#cb257-11" tabindex="-1"></a>        <span class="at">panel.grid.major.y =</span> <span class="fu">element_line</span>(<span class="at">size =</span> <span class="fl">0.4</span>),</span>
<span id="cb257-12"><a href="#cb257-12" tabindex="-1"></a>        <span class="at">panel.grid.minor.x =</span> <span class="fu">element_blank</span>(), </span>
<span id="cb257-13"><a href="#cb257-13" tabindex="-1"></a>        <span class="at">legend.position =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span></span>
<span id="cb257-14"><a href="#cb257-14" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;No quota&quot;</span> <span class="ot">=</span> <span class="st">&quot;springgreen3&quot;</span>,<span class="st">&quot;Non-effective quota&quot;</span> <span class="ot">=</span> <span class="st">&quot;steelblue1&quot;</span> ,<span class="st">&quot;Effective quota&quot;</span><span class="ot">=</span><span class="st">&quot;indianred2&quot;</span>), <span class="at">drop =</span> <span class="cn">FALSE</span>) <span class="sc">+</span> </span>
<span id="cb257-15"><a href="#cb257-15" tabindex="-1"></a>  <span class="fu">scale_shape_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;No quota&quot;</span> <span class="ot">=</span> <span class="dv">16</span>,<span class="st">&quot;Non-effective quota&quot;</span> <span class="ot">=</span> <span class="dv">17</span>, <span class="st">&quot;Effective quota&quot;</span> <span class="ot">=</span> <span class="dv">15</span>, <span class="at">drop =</span> <span class="cn">FALSE</span>))<span class="sc">+</span></span>
<span id="cb257-16"><a href="#cb257-16" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Satisfaction&quot;</span>) <span class="sc">+</span></span>
<span id="cb257-17"><a href="#cb257-17" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb258"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb258-1"><a href="#cb258-1" tabindex="-1"></a><span class="fu">ggarrange</span>(Euro_plot_1, Euro_plot_2, Euro_plot_3, <span class="at">ncol=</span><span class="dv">3</span>, <span class="at">nrow=</span><span class="dv">1</span>, <span class="at">common.legend =</span> <span class="cn">TRUE</span>, <span class="at">legend=</span><span class="st">&quot;bottom&quot;</span>) </span></code></pre></div>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAPACAIAAAB7BESOAAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nOy9X4gjV5b/eTys62FczDhZ29kQilndgp97MQmGwlNB5iKa2JdmCjq2t4nMhwRH8zPTuGLmQauHzQdHP+jBUQ/ZC4EexkrjH91U7JILmQFbEwuerZe5DNqfksifScagn+lxg0NYurBZVU31NFXdUP3gfbg3/ioUKSmVqfxzPhR0SjciFJJbX91z7znn+8p3330HCIIgCIIgCIIgCIJcbP5s0TeAIAiCIAiCIAiCIMjJYACPIAiCIAiCIAiCIJcADOARBEEQBEEQBEEQ5BKAATyCIAiCIAiCIAiCXAIwgEcQBEEQBEEQBEGQSwAG8AiCIAiCIAiCIAhyCcAAHkEQBEEQBEEQBEEuARjAIwiCIAiCIAiCIMglAAN4BEEQBEEQBEEQBLkEYACPIAiCIAiCIAiCIJcADOARBEEQBEEQBEEQ5BKAATyCIAiCIAiCIAiCXAIwgEcQBEEQBEEQBEGQSwAG8AiCIAiCIAiCIAhyCfhvFn0DCDI33nvvvUXfAnLp+eKLLxZ9C8glBlUIuc6gfiKnAfUTuc5MpZ8YwC+awNFsqlp+Q5nTBZnnbA/WWuOux7y66YLRbunSKV4kcDQb8jfNAm9v16UhAABRja1G5iXGjjKvbrphwYuQ094lgiAAIGQm/+QF/YIxr2661akkkXl1c7CZPSOlKyNvNP1x5MS3ZAhBkGtLbqJyQdVzJqbUz5KhlHxepQ8IQS4iGMAvmKBLAYB2g4Yyn5kiO+jQsLo2l2uNewmvblMANfts4JipECGkrkkHyfy3fHSu4A7AhLxy9CH/47vbny72Ti4IV33tPxuPMq9umtpZfQvPk8Ax3TCrR4Gj2ZQYbV+XAAJHS7/RzJpp4Gi2Fq9FlgxNCarQBQfVb+5cYf3kIWskKBCpZ+dKxKjT6edJQyn5PMXPC+rnYkF5PH9m0E+sgV8ozNulQAgBuuux83lJSW/5/uw/OixwCvfLA8emAES12r7v+77ftgwC1HaCk0clveVnsVQAIMbW5f9pvKC8cvRhLNCjD5FrgaS3LDX5jl5SAkcbzS3guhoLiNKw1FhixdiGmFYqG0Ysv8zbpaBa0YwzcxpyZUD1Q6YjcHj0npo3SXqrbZDQ3bvU6jm9fp40lJLPDYMA7V7yz+f6gfJ4icAAfpGwg04I6uZWjUDYOUjPE5lX17R6eurIvLqmJXNt5jl1TVB3AhYfY7ohALXF2eIs5vGDnYDlr8wCpx5fSas7ZdPVwNFMm4JqGbndd2CDPgAxthqK+IWTFH0r1u/y0dFXsSmG7why5qSjVwAQehFpQfbpuscCZ2RMCEwy4gR8UjhyjfTT2ZHA0TTHi1657g0z98jPqhfqEvPqNiVGu22QzPPDQQhQlRMBqcgjEltwtYNOCESuJM9MdBqCIFeZoFs4IZFWa0SVKyeJ54i+BafXzLGCPDpxFNcoXKadQT9LhiS95V/+dC4EuTxgCv0CCfbcENRNRaoMiOt2Dpg+YcyaK8YKqW2WZXr2d7dF6blckWAw/kI8tx3ksZeSVavdUCQInInuEwD6AwbjJb0/YKBk3/Ppw/enT5/Oeuq14M1vrcLnXzn68Mlf2ed8M8hCkeQqAB0MAaSkPYaviwdmHZJNp9A1u5bvN4BnSppOIhPUFiOBo9m2RonR9n2JX2/bW22JXEu7H2egMq9upq8A1O0Ybb8lBjvxDQaOTceXokt6y9cBgHmjY5lIPHPOpura7l6g80TPPTcEYqxKAAwgOzflH09/ok8yD6rQxQTVD5mSoEsBVHl0QiLprUb0d7l4jurb6TWzUJCl1Vp+LsmXHzaKBHQW/SwfSmDethsSY2vGgB71cyGgPF4ucAd+cfDfhTWFzylh8mwsvlOU5KMbBPhmtqS3+CPVSqfJhyEYbd8fzZxnB50QIL5Q+lKFKHpDKY6rJbkKELrbXhDtzAXedrw0UD6auSNvlwKom7j7jiDnSjThEl89Sd/KZYim0s7XVL74lh9R1lRIvr7Sao1AOBhClF2ZfK2l1VouAafoO8+8+mwNPkd2zoeDEMStACgNv230bb51ZfeNthDG4aBIkuLTEAS5fvD8wfKY9UTxHNG3OWhmoSDzuWRK+4IuBVJbnWpCVaKf5dIqXtLRNM1MfyAIgpwBuAO/MJL4HbgC06la2YX93T1vsLa6qkTLqOMZFxCLMxljQXAw6A46lBZOYSdA2TAIdUPXNt2pRxPEdljhavHEvPHGG6c5/erz7dgR/OiuL8NBtBMt4Js5iSZVC/agoGCkaK6baFQ66adaelLgmG6oWq0ZxEDssic7Wekqz7gJU0sS96O58283j1+lCwqqH3I6so4exGi39BPFc0Tf5qCZYwRZWVOBJhlGXQqqNWUcXaKfpdIa3UEjyi2Yvc0ffhkXA8rjpQID+AXBt5qB2lpK/yaM4LmG0pCGIXVdACCqsbmhj9kcL109HmvhNjWS3mrLzrYd+cRZWxuD7TheLx+N4GsauP1+tnx3+9PCriTYbvQ6wwZ9gNA1tdyXsjqn66dmvarlNypevXwxj9pdwyDg7nobygyCoDR8CzSbvx1itNvGttmRKyNNmEDSW9ZAs3e9DWVVJkWXmihhFLkkoPoh05EpMwKI49MobR7OTDyn1UwB3y7pBg1FEX3lpl+bHKufpUNF9zFFaSiycFAeLxcYwC8Gnrw+Ap1wuqo0/PZaYqseUteexZWN24cAUdWavCbLlYoy3Cuwi54USWm0/LgoDIJumC4dKx/lT6VyEhAEOWtSXzm5CgBn5YokOlskVz+xLxwxNnR9OEht9kxLPM/mNxBCdVMCCAYjulORCdDBEFYBcm052KBflnmAIMiVpyKTwnY9CdJZiOf0mhnfTrT9Xxl0Zi9DL9bPk4Yy95Ff+kAQZJ5gAL8QeBW70W7pUmKdGTiaTcV65aj08e6fySUkRW8oegOAseBgz3bjBPzf/w7gq69hXOu44x4DgN4x6BIb9HPe0OygP9kbeAIFL8KCZE2BqOqbNLN7VT4KzPuoFeUkENXaGldtj8wBvp6KVp/Xh6+/gny+DwBR74TxkllFJkCz3Y94D6V5zUrThZh8AbNafOBxjwEs/VECkBqWSpOGc+MpEr1UdhFR74RiI4rPxrsfa/ahOE5dV0MgtQpIlRpxXS66zKubg00L+AhylZhQ/VAeEQBI2l5+Dv2dVLoiMZq16O+zEs+JNTN3xzyC9+R+SGpbE9zB5PpZMsS8uulW057w/7qECUyXjknkEbXxgoBN7BYBL/WurUpiC4y3JlHWVMj2B6HdxBwktTEeONyNhI9JUkWWSSyUx9++AHjxdOLVWtpNOss5vLNcqj1VIcz7rDf63M8003bjGvqQ0kOAZfH7Uz4qpL/3HOJh2yz2jULmyHe3P+X/4me45yfafl5Rltf9FPdISA9heV3ExqKT5nb0veP93+dUz1KRSapLJxvTwrIAbnR3gld98KuHz3LPfKRppvuC9+5sq0/oIdz6vgIAIOmbKxDSw1v3xKdwi+5TWNnUJfERUNsJeGrSk3+c4yeAXDBG1S8GnZCRDEqjefsG3XHZ7Wbc71d94jbdECq1VelsxHNmzYRIyVw3nKh93TT6WSatqS57vDb+2bMK9rG7nIyTR9TGCwXuwC+AoEvF2iovyCQk5LnzaypQ0X6Ed7Wjrkl51RNRVUJFDM9ri3Id4TItVJ5TU+sb7VZBb7vlFQmOYGUZxDptSG1ziqR5FjjbNn088vzw18cFRx+LHILyUb6gAcsAx6rlNyqBt227IdZOnSc5FeYPcXn1anG8r2n7qcfqukr3953gfR7DKw2/LdfNuJJzjp3dRKV5lAFAjLZVNe3StNT4zC2jY7q2s1Z8M9mOUgI2eAFwAx5HVanqukr3H3nsri7xsaWlb3Y0bQcAAJaW4NkLcSdKw1//SrO5X06vN//edgiCXDYCp3n0khj3qm5T0+Jn76jkkEYFNvMXz9k1EyBqi3zyGsK0+lkmrZLe8mVHSz6ipR9P2z4PQZDJwQD+3Ek5pTGvE4Jqbcm7Ju/1sWEQ6tJu0KgMdikskcqzcAhAVGOrceszncIfeJaTpLeavZ/dPzp+CQAAN26ufPCL+3elbEu6sPMl07/XYwA3BsdiQLXaP0hS6EHSt6xfN+3DJLqurJBhLxxXtcTFnqiW+pVNMyE5GzwBqKh3XqWHPEeeqJs/lXebopVK6ah45duvDo+IXAGQFN6kD2unEGROvP2OWB7LTiqZ/AUd1ycul/tI5Eoy2SO55NBkRLV8fehoZnxc9IpKw1KpOCh0zV2j7TeSK1ThoK65IQAQw6pJcFR9O7r0nhuOS8RkXt2mxGhvwbbpxmeIeqP0u2Vefz+d4vr6j9oPxJ8s3RqKefX94/iC1imm4OhjfElBJ2QkB3dS39LvSvrdRup55tUBKqxompLXq6y+8aueUjMLBTmyo+d7N7w6KqnSzN/lDPp5krSKhYxOzai6bv8vTvxsx4H6eQFBbbxoYAB/7iTuIMGeG4K6qUiVAXF5BB+NsQEAvF6zHrTifkoryayWefXmURJAv3ze29kL7ub0mdTelQBWJDiC/gMe1hO5IqV24AHYwe5hJhAf9kJQx89bZdVqNxQJAgfo8Ttv50Zflf9jy/95/JB5u6L3S/moxCvIjv4Dv3a0xDFz7RRK/7SgLl9XMr02osmfH80EzTokcXroml2L9y4KHM02HTmexFFbjASOZtsaJUbb98V6YuI3xC3XdVFTUzfTVwDqdiJjN2BeJ75Bnos6bjcr0lLmjY6N04+oojWyWeLOlauRgfP4CyIIcg3hnT4LWllKeiuO58vFc1TfTq+ZhYLMq987B0fQocRoR0ZyY6x5Z9HP8iGe6w9GW5f3JumYjyDI7GAAvzhSHaCzc8qT4C3w4kZvzKubLm9hp7f81UwnEQYAEIYQ/wpkWpnytihqFDhnLlX0woreKHoaokDA3fbkLW5nxwJes0VOHgWQ9C1rsG2ncvmJap1NO2wEQYpg3rYbEqOtR6HsltEx05qUTAKVNRVoKpczHsnmbfLJ5GAIIAk7o/grnbdJLnaPZF59zNbRCYz0lBoOQgAQCxVik0hkepJEG+cJuuZeVtAJGUnDBv0TtxNOFM8RfZuDZhYKsiS/BtBzm6FqcVnj8fsElfApSvSzXFrj8F2C0q4lJ4PftYsIauMFAwP4hZExTeMqPpkNPCfs7+55g7XVVSXZ0h/HuEoocSZjLAgOBt1Bh8Zt5qaF1+WHucL8yUYB2LDbz7xyOGmpVwEoJVODuowMB2F2opefMI63U8uOFM11E41Kl/lUS08KHNMNVas1Qxq72GVPdrLSZZ6Bo9mURPthzKubmovF7ogAnZCRcrJl48Rot/QTxXNE3+agmcWCrNy3VM2m0cwy6GZWASajRD9LpTUJ35GrCGrjRQMD+AXB08Rzxk4TRvBcQ2lIw5C6LgAQ1djc0MfGuyWrx5mq+VMh6a227GzbkU+ctbUx2I7j9dJR5m3bNCSGFe/PO9u2m8mwRc4S1GWEDfoAoRs3YYqozun6qVmvavmNSrryvAhqdw2DwLgC/ZNQGr4Fms3fDjHabWPb7MgVIbwk6Y0sWkXN+DIIglxtRix9Yxf0uH3GGYnntJop4NslvPsQ38Wffho1Vj9LpRXDdwQ5TzCAXww8eX0EOuE8Umn47bXEVj2krk0H028icbckIKpak9dkuVJRhnsFPUknRVIaLT9Jsg+6Ybp0bOworwgwtuIVCElpbKrUniohAUGQqUmlAclVAJif63vudRybZjrfnegRSYwNXR8OUps90xLPs/kNhFDdlACCQZgvaK3IBLBlJhIzoVE8cj2oyCRu11OMdBbiOb1mxrcTbf9XBp2QGFuzTaKK9XP8EPM6IYRhZhXDNbXOWf2mIIsAtfFCgQH8QhAxa7ulS0mL0MDR7Ki6aGTVl5caVZNLSIreUPQGAGPBwZ7txgn4v/8dwFe8W30Rx0kXejbo5+xO2EF/sjfwBApehAXJmgJR1TeznejKR/80eOTUH0ajxk9P/M1E5grq8tXm668gn+8DQNQ7YVzGM1LcGPVQmtf0K21JzBcwq8UHHvcYwNIfJQCpYal0kuYgRaKXyi4i6p1QbETx2Xj3Y80+FMep62oIpCakKLXr9aSL+nONGWcRXzKKXEWitpefQ38nla5IjGYt+vusxHNizczdMY/gPbkfktrWBHcwuX6WDK3WiBuO7EuN9TRCFs7Maobqd0H4s0XfwLWEdz6urUpiCwxoN+B18BB2DpKFVtr1+APm1VMb44GjaVrdC/iYJFVkmcTR8PG3LwBePJ14tZZ2xXWABc62GwIPnMtg3me90ed+ppm2G9fQh5QeAiyL35/SUUmuAgzpw9So24w/IOQc+e72p/xf/MwrRx/yfwu8K2ROLK/7Ke6RkB7C8rqIjSV9U4XQ3RaSI/q/n+gjPBkVmUDo7onGRoy3sJwEZcMgQG2ntCVS8KuHz3LPfKRppvvCaPu+77fVJ/QQbn1fAQCQ9M0VCOnhrXviU7hF9yms8PcZrab6bYMAvEZNrfyFketDTgZRFa8RSqN5+wbdcdntZqSebfWJ23RDqNRWpbMRz5k1E8T9UNcNJ5pETaOfpdLaSv28WCoAwLj+98hiQTW7GuAO/AIIulSsrfKCTEJCnju/pgKlfL+Jd7Wjrkl5QhJRVUJFDM9rnHId4TItVJ5TU+tHZqBZUjZy0mqNuCFNNX8/GRY42zZ9PPL88NfHBUcfi9XX8tExvDa2aRZyDuQEnT/EldfLzPG+pu2nHqvrKt3fd4L3eQwvurPHOZBz7OwmKs2jDABitK2qaU+SYiMaOtvOWvHNZDtKCdjgBcANeBxVparrKt1/5LG7usTHlpa+2dG0HQAAWFqCZy8GDBTgBaMNRWSr/ncqOcY6HgS57gRO8+glMe5V3abwrgAAuKOSQxp1kpu/eM6umQD55vbjmVY/y6Q1w9dfAcDSj7cwfR5BzgoM4M8d3r5O3dRF1ZBqbcm7JreB3zAIdWk3aFQGuxSWSOVZOAQgqrHVuPWZTuEPPMtJ0lvN3s/uHx2/BACAGzdXPvjF/btStiVd2PmS6d/rMYAbg2MxoFrtHyQp9CDpW9avm3bKCr6yQoa9sUlPXOyJaqlf2TQTkrPBE4CKeudVesiz4Im6+VN5tylaqZww2geoqD/+D/1oE56oP363//BhNwCcOyPIHHj7HYDj0Uklk7+g4/rE5XpfErmSTPZILjk0GVEtXx86mhkfF72i0rBUKg4KXXPXaPuN5ApVOKhrbggAxLBqEhxV344uveeG4xpxMq9uU2K0t2DbdOMzRL1R+t0yr7+fTnF9/UftB5GvZtwaKmXnIektX2de/RBm5enTpzOfi1w03vzWKnz+laMPn/yVfc43g5wz3IltS78r6XfTRrrMqwNUWNFkKa9XWX3jVz2lZhYKcmRHz/dueHVUUqWZv8sZ9PNEaY2e/eIYiHH/g1nDd9TPswPV7MqAAfy5k0wTgz03BHVTkSoD4vIIPhpjAwB4vWY9aEUCyFaSWS3z6s2jJIB++by3sxfczekzqb0rAaxIcAT9BzysJ3JFSu3AA7CD3cNMID7shWIPqhBZeMYHDtDjd97Ojb4q/8eW//P4IfN2RR37RKM/bDQ+aCSj9Yez1sCj9J8elPjrQabXRjT586OZoFmHJE4PXbNr8d5FgaPZaZMIaouRwNFsW6PEaPu+WE9M/IY0ux9brjOvbmZsJqjbiYzdgHmd+AZ5Luq43axIS5k3OjbOeyOqaBV19bycqcAnWVg7z9gBCkGQqwHv9FmQDyjprfSMpUQ8R/Xt9JpZKMi8+r1zcAQdSoy2AtHyQ2Ei+yz6WT4UfWR7bji9ex2CIFOBAfziSHWAzs4pT4K3wFOtrYYSORm7PNVTb/mrXt10q9GMlwEAhCHEvwKZVqa8LYoIyiF7qaIXVvRG0dMQBQLutifHVnC8ZoucdhRBkHNABKyRC5BIXU9pUjIJVNZUoKnltXgkm7fJJ5ODIYAk7IziCV3eJhkKC0aZVx+zdXQCIz2lhoMQAMRChch2FcmwJNHGiGhji5zCE+mNN96Y8UzkAvLt2BH8D33FYYP+iTHrieI5om9z0MxCQZbk1wB6bjNULS5rPH6frp1QiX6WS6sgNbWdFfxanSGoZlcFDOAXRkbkuIpPU28Z9nf3vMHa6qqSyvwcw7hKKHEmYywIDgbdQYfGjeSmhdflh7nC/HmMTg1q0BxAib9uDAe5nej8hLE6tidFdqRorptoVLrMp1p6UuCYbqharRmmgWKXPdnJSpd5Bo5mUxLthzGvbmpudpVAuCQFjoYuSAgAwHe3Py1s8oQ9Qa4h2bJxYrRb+oniOaJvc9DMYkFW7luqZtNoZhl06fRb4SX6WSqtIG53d+yeP3IBQDW7MmAAvyB4JXzO2GnCCJ5rKA1pGFLXBQCiGpsb+ths85LV40zV/KmQ9FZbdrbtyAnO2toYbMcR+WlGkfMHJf66wQZ9gNDNuPgCTGhbNAGpWa9q+Y1KXHk+Bmp3DYPAuAL9k1AavgWazd8OMdptY9vsyJV4dhn3VhKtoopehi8sjpR3IghyfRix9I1d0OP2GWckntNqpoDrFu8vxHfxpw+lx+pn6RBnZDUDQZAzAQP4xcCT10egE05XlYbfXkts1UPq2nQwfapp4JhuCEBUtSavyXKlogz3CnqSToqkNFp+kmQfdMN06dhpRhEEOQ3FPvDGvWqcBiRXAeCstpsDx6aZzndlRpXHPQZwU93Q9eEgtdlTRpGPcUUmAFwfOwdfVkOobkoAwSAEUP+4p8UfhXrPIJkJenTLmv1fCfoYIxy+dok+8NePikwA+gPGBttmsQ+8dBbiOZVmZoi3/yuDzmRtPCbXz5OGIPjIpgDgmpo7Tx8TZK6gml0NMIBfCLyK3Wi3dClpERo4mh1VF42s+vJSo2pyCUnRG4reAGAsONiz3TgBv0iLUxwnXejZoJ+zO2EH/fL7DrztXTfkP2L/5R8DpmS2/bnHnGg0v/Jarnn0aUaRcwcl/sqxvO5/9n78iHl1cycp4xkpbox6KM1rVpq2JOYLmNWSo197QwKQGpZKT24Own2MlzNPRXnyUTMoCiuWAmLuSffj0vrA0WwXgNQqfENNtA/hqaFLv0MZQjiohNcVkTVumul+Gcyrm033hpignZV4TqeZyR3zCN6T+yGpbZ14B9PoZ+kQBI5m924C/HU0odUAY/iLy4lShqJ3wfmzRd/AtYR3Pq6tSqISHmg34HXwEHYOkoVW2vX4A+bVUxvjgaNpWt0L+JgkVWSZxBPN429fALx4OvFqLe2K6wALnG03BN4bvvC2HdOOoncAeN6zzboXnezVNc204xr6MOz1AJbF789pRpHz4JWjD/m/3PPf3f6U/1vIXSFnCO8+FCPpmyqE7nb0jeb930/0EZ6MikwgdPcC/ojxJpWToGwYBKjtBOMOCBxNs/Nub8xpUQDyQ9G+edAHAPjdgAGAtFq9CQBLfynC8spfLgHAzeqqBNJqjXApjio7nz2rGGhkfN3JCWOhTiLXDL7p/pIrx5mI58yaCeJ+qOuG5KRZ1LT6WTLEy5PeIs/FXJRr96438VwUuTig6F0KcAd+AQRdKtZWeUEmISHPnV9TgVK+38S72lHXpLzqiagqoSKG5zVOuZ5vmaKj59TU+pEZaJaUjZy0WiNuSG1zoqR5XrVPDGtLV4aOZlPyFoRhtDsm3Vq6AeHL3Dmviv89zShytuR0mT/EiP0qcryvafupx6rVlnfNuMxcafjNf9ebcSUnuRdtnvC0nd8mNZk3Ad5MRv7wdeDY8QojgS9jT+MbYstIVJqnUvjfIvBYNLL/+iuAF71Hdfch90m+t5S6R0l+EyCkdv37futu/g1FPsY/7dWbR6nnh/Ac4Ga4o2k7/I3eM2AnqmaX3wR4FR6a2kN+8NISwOuyuEtfdjTRnh4Aln6MTkgIcs3hjdzbLXlPS5e5q/eM/r/BBn8wXjwL9O3UmjlWkBMf+KS5/ck+8FPoZ6m0AgD86XcAr8/4QSMIMg0YwJ87PBBWN3UJmNcJQbW25F2TS+CGQahLu0GjMtilsEQqz8IhAFGNrcatz3QKf+Cp8ZLeavZ+dv/omAe9N26ufPCL+3elbEu6sPMl07/XYwA3BsdiQLXaP0hS6EHSt6xfN+2UFXxlhQx7hVWfIuufO70NAQCqPzFgxxWTcPbNs5fwFqk8DocAAISoP3y3v/NQCPtpRhEEOS1vvwNwXDCJC7oZH/jmkSTSPplXN3fq3rstXRKrfuFOdzOxHaZ/LUsQjex3/zb2NKah+6iWeBqDyECPJsGJp7EbNUd++x0AevTlT9I+yUfVt/lpjn04gQ88SPEZEeQnqfxV5tWTczZV186k0FPyo3j1U9kwCO3U2i15T7P7fzHzB/706dOZz0UuDm9+axU+/8rRh0/+yj7nm0EWQewDH3evS4jXFMeLZ7G+nU4zxwuy8IFnugyiOmoSH/gp9LNkKJbWB9zAbs+dvZsd6ucCQdG7LGAAf+4k7iDBnhuCuqlIlYEQXT0aYwMAeL1mPWjFOrmSaCzz6s2jJOp++by3sxfczc1xSe1dCWBFgiPoP+BhPZErUmoHHoAd7KaidwAY9kJQraLZcsasjv+SMa8ee5kMByGAuvnJf0pODZydhyI2OM3o1KD0Tw4qNSJAH/joUwCjrUswNmkfQZDrwyXzgdc3VdfuPPolUGK04/j9nHzgy6QVQZA5gwH84kgZwfMuKSf1aorgm+GqtdVQIidjl7ew01v+aqoTk2hcGoaQar6SvxCoVruhJJ1Z3AnN7HhRlrrJX6fgRy5q3goKnGIU9R9BzgH0gU+H76fmjTfeOP1FkMXz7dgR/E98PbnYPvC8q9LDhysW7zJ3nj7wJdI6LfjlWiQoepcEDOAXRip+j5ZhJ4ucOeo6W0EAACAASURBVGF/d88brK2uKpm98ULGdVMRZzLGguBg0B10aNxI7gQYd6Ar3qxfMCgxU4BKjQAA+sB7G8rqwdzCd+Sq8N3tTwu7N2GXkOvCJfOBF5vgriiJP0cf+BJpRU29VKDoXRYwgF8QvBI+58w8YQQvSo1CGobUdQGAqMbmhj52s7ok/StTNT/prQvDN2K0L2D4jkwDKvX1JrWKeHF84AEAgBjT+MAXkalYDZyMD3xm76oiE6CDIYNOCGGYmYW7ptY5q88EQZBLwMnpgJfeB76IYv0sGRovrTOVQiIIcgIYwC8Gnrw+Ap1wtVJp+O01b2/X5TvmIXVtOhCpSpP7wPMkVQCiqjV5TZYrFWW4l84OG73t4Jcf/eLhM9EwfjBkIImiL7kK0P/9l07djLzc1ffejPV8gtHoJQJHs8GyYOS3AEGQWfn6K8gvFwIQ9U54AX3gj3sMYOmPk/vAF4teanGSqHdCsRHFZ+PdjxPvJHVdDYHUKpLe8vVseiyMz15Crg18NRMtka8rImt873Po76R2O4jRrEV/X24f+Gn0s2SoIhOA3/zT32n2kA8Z7c1BCKRW2j4AuZCg6F0K0Ad+IYiW7m3ft1QAAFAt/mdkAy/JVYBwMExOybk2g6TojZbv+367bRkqCC954QM/4V0M+gCq5bcaDV1XFEWShK3nGIKPTDuO3gFCmvKBr8gEwoc7KS93un8Yb/5PMNo5YInf/W8n6BuDzIdRm3cU6yvK8rqf4h4J6SEsr4vY+JL6wAMABL96+Cz3zEeaZrovjLbv+35bfUIP4db3FQAASd9cgZAe3ronPoVbdJ/Cinifou+z73M5xiQjBEGURvP2DbrjstvNSD3b6hO36YZQqV1uH3iA6fSzVFrfW4bh0WPxIVlvuuYcf0KQBcAnh+MmhNwcHv3hFwjuwC+C2F+DeduJD3xrTQWaWcalXbEhHwW24nTeKsTilm6SVJFlAgCpePfFUzYuaynThR4AaDfYUHgzvMDhvxHF2WLMafUAbty+12relQBY4G3bbuID/+cAAHDj9vpHzfdvw9H/Xr+/H768sXSL91YtH5WrANRt/i//z/E3jwEAvvq/3OOZ/UeQqUEr+OvBiA/8ukr3953gfR6liurJOId8xvZDReQ8jYnRtqqmPUmXStHQ2XbWim9mZMscAIANXgDcgMdRVaq6rtL9Rx67q0t8bGnpm9jJeGkJnr0QZpiDflmzPuRagvJ43Qmc5tFLYtyruk3RYB0A4I5KDmmkFvMXz9k1EyDf3H480+pn2ZD3xTFUbr91FH9INwHexG2YKwhK4gUBA/gFEHQpT45iBxkfeB7Bu3uB3qgM/isAAHVNyn8QyB1y4zDkWU7KhkGo69qZjiZ8PUBkNr2kpvafbze9JpT5wEurNeKG1DZLkuZjjj77z88B4OXRjikmvhy6+3lDuQvsDwAA8PJov5mKEV4++4bBbemEUVAa68t0X0TvAHB8jKmrCDJHxvjAM/kLOq5PXC4DhsiVZLJHcsmhyYhq+frQ0cz4uOgVlYalUnFQ6Jq7RttvJFeowkFdc0MAIIZVS7sSB3tuOC4dh3l1mxKjvQXbppvyMebGlKl3y7z+fnpt9PUftR9Ethyp1lDDQeyrAUrD9wteEkGQ6wXPy9nS70r63bQPPPPqAJXCvZK8XmX1jV/1lJpZKMhczYx2SweIqqMCRxvjxjmDfp449DeffNJMLm+6mcICBEHmCAbw5w5vX6du6lLg5HzgWxsGoS7tBo0NeA1giVSehUMAohpbjVuf6YclKVR3NnUp2+Xk5bP/j8H3AABe/ut/cp8BcB/49CHS6rtvueFjyBEWdB1h3yS581le+28BhOTfWVef7NOoNmr9Tbp/mHJ6HzsKAG//8M76r5/si3d4c8X6xezL1+gDPxVoBX+NyXRYjiZ/fjQTNOuQxOmha3Yt3rsocDTbdOR4EkdtMRI4mm1rlBht35f49RK/Ic3ux26WzKub6SsAdTuR+xAwrxPfIM9FHbebFRlwMG90bFwFjpT17IyzoYDXFBHY1TTxf/v8MsU0oApdDVAerz2802dBXo6kt+J4vlw8R/Xt9JpZKMi8+r1zcAQd4QMvlh82igR0Fv2ceWg6UD8vLCiJFwcM4M+d2PUtcAp84MUYGwDA6zXrQSv6BWAr8bZUkQ/8YTeAIh/4FQmOwmevpnzgUyn07ODLx6M+8GHkbVd41zHiWOu+ApEP/H//PzZ+/n7qJ62/f5hyeh83KgGAov9cAXhfrBb/9f+E9u8Icq4wb9sNSeyiJlLXU/3jkkmgsqYCTeVyxiPZvE0+mRwMASRhZxTPZ/M2ycUZN8yrj9k6OoGRnlK8g4hYqBDZriLPkyTaOByEEKqbvrCeZ17d1AbzqyNAEOTSwSZoyHOieI7o2xw0s1CQJfk1gJ7bDFXuAy/i9+nKEUv0c7YhnM4hyBmAAfzCQB/4MwINzKcDreAR4LOtzEQvP2EcXxueHSma6yYalTaurJaeFDimG6pWawaFEbvsyU5WusxTtBAR+2HMq5uay1cJMu5I0VVmtDHG784VAeURyZItGydGu6WfKJ4j+jYHzSwWZOW+pWo2jWaWQTezCjAZJfo529D04Jfr4oKSeGHAAH5BoA88cjFAK3gExD5T6GZc0AEmtC2agNSsV7X8RiVVeV4ItbuGQWBcgf5JKA3fAs3mb4cY7baxbXbkihBeYmxF1xStosa/TFFBEXJtQHm87mTKjABSLuhx+4wzEs9pNVPAeyR1g4aiiF386adpY/Vz1iHkyoCSeHHAAH4xnKEP/BRM7QMfR/z56lAp4+XO4T1NRp3eR0YRBFkIqTQguQoA83N9z72OY9OMaLDy4wGIsaHrw0FqR2daMvvpgRNCdVMCCEZ1pyKTzAQdQRAkoiKTcdY8AuksxHN6zYxvJ9r+rww6ITG2ZttmKdbPUwwhCDJfMIBfCJEPfEuXkhahgaPZUQnRyKovD3irySUkRW8oegOAseBgz3bjBPzf/w6Ad6sv5DjpQh/5wCdxPzvol9128LFmHwIAX23uxFX4ICbB/7z3cYfyRnSE3Kn+KeP0Pn4UgLvS7bohX9X4L/8YMAXL4M8NvnQar6riSurV4+uvIJ/vA0DUO2FcxjNSwRj1UJrXrDRtScwXMKvFBx73GMDSHyUAqWGp1M7UkhZTJHqp7CKi3gnFRhSfjXdjJQNQ19UQSK3CT6iuq3Q/+pRuLSdVTsh1JSePyDUjanv5OfR3UumKxGjWor/PSjwn1szcHfMI3pP7Ialtld1BSiTDvFVnulRgGTIb+bMNIVeFUUnESeNC+LNF38C1hHc+rq1KYgsMaDfgdfAQdg6ShVba9fiDER94Tat7AR+LfOBFNHz87QvhAz8ZtCuuk/WBL7rrj+I5LwAAhNQ26+IG+YrDkMaN8sPwkA5hWfz+lI8CBI5pR9E7ADzvpa6MnBPf3f6U/xt3wCtHH/J/53lXyPxYXvdT3CMhPYTldTFnk/RNFUJ3O/re8f7vczJzrMgEQncv4I+Ytz1p3Y6yYRCgthOUHRT86uGz3DMfaZrpvjDavu/7bfUJPYRb31cAACR9cwVCenjrnvgUbtF9CiubugTSam0Z6D9Bm4+0f7z0zXF0GoJkQCW8RiiN5u0bdMdlt5uRerbVJ27TDaFSW5XORjxn1kwQ90NdN0yvAIzAo3ditNsGAVgGamtCaANH0/jGku/7bWMZjgFewCmGkKvGqBU86uH5gzvwCyD2gRcFmYSEPHc+8YFXeFe7lA+8qhIqYvixPvDxg+fU1PqRGWiW5aQL/VQ+8OyXn/RGnw1d8yPZv68AVOS3APKOdN9Ff5SO8n4AxLC2dGXoaDYlb0EYTrDvhpwTo2INuOZ6+Tje17T91GN1XaX7+07wPv+eie7scSXnLP3fxyAqzaMMAGK0rappl6alxmduGR3Tze8OxWQ7SgnY4AXADXgcVaWq6yrdf+Sxu7rEx5aWvtnRtB0AAFhagmcvBgwUaTg4hrcINaM8hTsqOaT/FsBdlCEEub4ETvPoJTHuVd2m8K4AEPIQdZKbv3jOrpkA+eb2hQR7bgiq1dIl5gHAO/eMP98RffcqcgVgGKdsqZb1jm3vekzRpdmGTvVJIAhSCAbw507iAw/M64SgWlvyrul2Dpiuxz7wlcEuHfGBp/AHniUq6a1m72f3j465NfuNmysf/OL+XSnbki7sfMn07/UYwI3BsRhQrfYPkhR6kPQt69dN+/A4vrnKChn2Cn3gH/1LbpMr4sVvAQDYQecxVNQ7r0bb7OSO+qfDKKusdFTUE2zpigQwBACo/sSAHXfSnyoEQU7g7XcAjkcnlUz+go7rE5frfUnkShIs53tgJCOq5etDRzPj46JXVBqWSsVBoWvuGm2/kVyhCgd1zQ0BgBhWLXbMBDHNHNOIk3l1mxKjvQXbphufETXYSL1b5vX30ymur/+o/SDy1YxbQ3HDpa1s1muqK/1UoI/xVQKtj68zQhj0u5J+N60HzKsDVFhR84y8XmX1jV/1lJpZKMiRHT3fu+HlP0mVZuaWeAXlmgJRw3vm1UWtPxx0hlmNZ56oEVidaWj6CB718yKDenhBwAD+3EncQfgK6KYiVQbE5RG8PokPPDCv3jxKou6Xz3s7e8HdnD6T2rsSwIoER9B/wMN6Ilek1A48ADvYTUXvADDsjfGGkz544H+Qe47XjG7eBYimzGs/byQ/cIGjUVHGXzqascHjPVCYVy+zrSoHpX++oFhfXTK9NqLJnx/NBM06JLOx0DW7Fm9QFDiabTpyPCOkthgJHM22NUqMtu+L9cTEVEiz+7HlOvPqZvoKQN1OZOwGzOvEN8hzUcftZkXSwbzRsXHeG1FFq8jv4eVMxqrEG4BUZSnX+xl33xHkOsM7fRbMRiS9FU9oysVzVN9Or5mFgsyr3zsHR9ChxGgrEC0/bIzo2Ij9cNJXmAHk5l/ZLsWzDSEIMl8wgF8cqQ7Q2TnlSfAt67iFHPPqpstb2Oktf9Wrm241mngyAIAwhPhXINPKlLdFUa121IsudamT78Lb5usPAGI1NzdlTpq3QunoyA9j5soIgpwDzNt2Q2K0hU6I1PWUJiWTQGVNBZr67sYj2bxNPpkcDAEkYWcUz2fzNslQWDDKvHrR1tHJjPSUGg5CABALFSLbVSTDkkgbGQAAtbU+n0oLi3hnxhgeHXGvFGh9fG0pmtuMHHOSeI7o2xw0s1CQJfk1gJ7bDFWLyxqP30sq4aN3ULfjQD/fMVkQDoYgzzQ0vcEHfq0uNKiHFwMM4BdGKn6PVHyyyJkT9nf3vMHa6qoyspI6wrhKKHEmYywIDgbdQYfSCfukMO5AV7hZfzpOf2VUkDmDYn0dGA7C7EQvP2EcnxGTHSma6yYalS7zqZaeFDimG6pWawYdELvsyU5Wukw+cDSbkmg/jHl1U3NVy2/wG8hYxG+qbolDPHJtQOtjJCbbdoMY7ZZ+oniO6NscNLNYkJX7lqrZNJpZBt3MKsDYt2RG9fDlByIIoB5eGDCAXxC8Ej5n7DRhBM8nljSkYUhdFwCIamxu6GPrxUtWjzNV85PeeuBs2zRM12rNibO7MjI7KNbXATboc3dIN/t8dU7XzyWmV+LK8zFQu2sYBMYV6J+E0vAt0Gz+dojRbhvbZkeuCOHNROkta6DZu97GFkDWt4kz2wYSgiBXghFL39jqPG6fcUbiOa1mCniT427QUBSxi186mYp70SfRe0UmRUcSuTLjEIIgZwAG8IuBJ6+PQCecrioNv73m7e26fMc8pK5NB9PnevJ1VyCqWpPXZLlSUYZ7BT2dMzfOI/58E6uiYqekpArKR0uvjCDIqSn2gTfuVeM0ILkKAGf15Qscm2a+2mUekcc9BnBT3dD14SC1j15GkQ98RSYAXB87B19WQ6huSgDBIARQ/7inxR+Fes8gQAdD6ftQOAvPXxe5luSsj3H58toQVfuxwbZZ7AMvnYV4TqWZGeLt/8qgExJjq0S9Pq9rO1wjXVPLTiH73Y+12Dj4jkpSW/5lQ7yyinl1c7DZlvuz9zJCLjSohxcBDOAXgmi83m7pUtIiNHA0OyrcHFn1HSlLkhS9oegNAMaCgz3bjRPwi+ayKY6TLvS8DWlatNlBv+y2g1i2Q9fUOnEVPoiq03/e+7gj+swTcqf6p3j1tXw0f+X6wNhqYBB/gUCxvhIsr/ufvR8/Yl7d3EnKeEbqxqMeSvOalaY3t/kCZrXk6NfekACkhqXSk5uDcB/45cxTUZ581AyKwoqlgJiN0/24tD5wNNsFILUKvFsjEKam4fw08j+/X/CSyPUhrXsofdcPUZBjmkm/DK6eTfeGmKCdlXhOp5nJHfMI3pP7Ialtjb2Do6a+E8JNtb3bkEQzPbD8hgLSam3ZdelhWiQpLNcqcMKQqN8f8ox8XpZVww34y0z5rA/1cLH82aJv4FrCOx/XViVRCQ+0G/A6eAg7B8lCK+16/AHvMBKf7miaVvcCPiZJFVkmcTR8/O0LgBdPJ16tpV1xHWCBs+2GwBdRi+76o3jRFQAAQmqbdXGDfMVhGLnEAYThIR3Csvj9KR+Fz+uZK4fUNTUnmPQdIHPmlaMP+b/c83z+ipJ9ReCN3WIkfVOF0N2OvtG8/3uZj/AUVGQCobsnvtO8S+VEKBsGAWqPV4PA0bSsLgEAc1oUgPxQtG8e9AEAfjdgACCtVm8CwNJfimll5S+XAOBmdVUCSd8ySHyXvHB+6cdbdyd9k8hVIyeDhaqIXEv4pvtLrhxnIp4zayaI+6GuG46WBMUw5387egm37u1yZzql0TYIn4kCAHw3esJ3cMIQf1VbE1mcT/5xjr8gyLmD6nfxwR34BRB0qVhb5QWZhIQ8d35NBUr5fhPvakddk/KETqKqhIoYntc4uXamICrTQuU5NbV+ZAaaJWUjJ63WiBtS2yxNmhewX37SG302dM2PZP++AlCR3wJ4nBuOpb5slDn/R8EvE41Wg5HzIyfQ/CFG7FeF431N2089Vq22vGvGZeZKw2/+u96Mc8jJvej7x9N2fpvUZN4EeDMZ+cPXgWPHK4wEvow9jW+ILSNRaZ5K4X+LwGORbvn1VwAveo/q7kPuk3xvKXWPkvwmQEjt+vf91kgoHfnA/7RXbx6lnh/Cc4Cb4Y6m7fA3es+AHVfskMlvArwKD03tIT94aQngdRnEbfqyo4n+9HDjdvPBBzj9RJBrDW/k3m7Je1q6wEa9Z/T/DTb4g/HiWaBvp9bMsYKc+MAnze2LfeDZo399DgDfxCIpnv6no4ayfNB5DES9k9Rc3VHJYWT2Pm5Il1JlSwDQ66ENJ4KcJRjAnzu8fZ26qUvAvE4IqrUl75p8drlhEOrSbtCoDHYpLJHKs3AIQFRjq3HrM53CH3hqvKS3mr2f3T86fgkAADdurnzwi/t3pWxLurDzJdO/12MANwbHYkC12j9IUuhB0resXzftlBV8ZYUMe0Vtm9ijf3lW/H5e/BYAgB10HkNFvfNqtM1O7qh/inW9bBQO+s/HXHm2DxhBkBxvvwNwXDCfCroZH/jmkSTSPplXN3fq3rstXRKrfuFOdzOxHaZ/LUsQjex3/zb2NKah+6iWeBqDKJOJJsGJp7EbNUd++x0AevTlT9I+yUfVt/lpjn04gQ88SPEZEeQnqfxV5tWTczZV186k0FPyo2T1U2n4foPPef/X5u1ZP294+vTpzOciF4E3v7UKn3/l6MMnf2Wf880giyP2gY+71yXEa4rjxbNY306nmeMFWfjAM10GUR01xgde+uCB/0H2fTqaTf+Hv7kd+cCv/TyxuYfA0Wg/ejB2KBZkbmZ8iugd9XOxoPpdCjCAP3cSd5Bgj9udS5WBEF09GmMDAHi9Zj1oxVPQlWSOyrx68yiJul8+7+3sBXdzaklq70oAKxIcQf8BD+uJXJFSO/AA7GA3Fb0DwLA3xsFtVO4jld68CxD1pFv7eSOr6yI2KBtV8i54LHBMmxKj/fPZ5B+lfzZQsq81194HPkXG4hNBkOvLJfOB1zdV1+48+iVQYrTj+P08fODRqwNBzhcM4BdHapbIu6Sc1KspgrfAi1vIMa9uuryFnd7yV/nap5j0MgCAMIRU85X8hUC12lEvutSlTr4Lb5uvPwAU/8hFzVtBgdLRkUb0AMRAR1IEOU+uvQ980sqTO82N7FlNxRtvvHGa05HF8+3YEfyPe5252D7wvKvSw4crFm/geUl94PErtmBQ/S4DGMAvjMwuD1+GnSxy5oT93T1vsLa6qkj5LewRxjUSEWcyxoLgYNAddCidsE8KixR/riVOw0FICAnDMHTtOszawRX1ZUZQsq8x6AOvpLfrJ9izQq42393+tLBpE/YEuV5cMh94kV/kipL4c/SBR64QqH6XAgzgFwSvhM85M08YwYsqzpCGIXVdACCqsbmhK+NmnCXimqman/TWA2fbpiEQoz3nDiVKw1fEK5iT+T8j8wMl+/qRWkW8OD7wAABAjGl84IvIVKwGTsYHPrN3VZFJeoIel7wiCIIUJQtmubw+8MIqs+jWM++YDfqjZu9FQwiCnBsYwC8Gnrw+Ap1wv0lp+O01b2/X5TvmIXVtOpi+VpRvowNR1Zq8JsuVijLcS2eHFd04j/jzii/JVYB+9tBhMlUuHx1B4n32M76qCILMzNdfQX65EICod8IL6AN/3GMAS3+c3Ace4Pe/A/iKd/iMSS1OEvVOKDai+Gy8+3HiPaeuq2m34s//z2RlFbsoX2/42mW5EzJy1REFOXufQ38ntdtBjGYt+vty+sDH7sQieSCWO37+0T9obuQ8tLICXCNLhqJrRqob2s4a6uflBdXv4oM+8AuBV7Ebbd/3LRUAVEv8FdnAS3JVNAaJyLk2g6TojZbv+367bRkqQOzgGc1lx3DcYwCsdwxi6VS1/FajoeuKokiScEweR/CxJsQ5dE2t7gTJmnBFJhD+897HdY1Tr3/c/U28+V86yry6pqV83wNH08z/9w8nfojI3MnZvKPr+9Vied1PcY+E9BCW18UU65L6wAMABL96mLPICD7SNNN9YbR93/fb6hN6CLe+z+em+uYKhPTw1j3xKdyi+xRWxPsMHG3nG4ilmdpa+Ssj14BYBtEJ+ZqiNJq3b9Adl91uRurZVp+4TTeESu3S+sB7tmg41Pb59DORO0l/bxl6vegHY32514Pl93SpfCjJxm8bBGAZUD8vP1z9ZpsHcsFEzTw7cAd+EQR7blRlyVuO8Nz5NRVoZhmXdsWGfLxUyk/nXZisLV2RACSpIssEQHiPHH/7AuBPT9mkPUFpN9hQeDO8wOG/EWOyxYKP4k0rAAAIqW3Ga8y8UIzGB4ThYQiwLCpJS0cjO3pnrd1QJNFw6ptjLENdFOPEGtdiLzkjPvDrKt3fd4L3eQwvqifjSs457j/nPI2J0baqpl2alhqfuWV0THfsZk62o5SADV4A3IDHUVWquq7S/Uceu6tLfGxpKfE/XlqCZy8GDBTe93lpCcRqAM8DmrEIH7ky5Cag/CFq4DUicJpHL4lxr+o2hXcFgDBAj3LH5y+es2smQL65fQHs4BHfH8rU7tOWs7HbkJj3xTGsrCQ/GCsr0PvCY+/rZUPcVslq6RLzAOCde8af77jTNHZCrgqomecDBvALIOhSkRzFOyoREvLc+TUVKOUJo1x/qWtSrq1EVQkVE1U+sXTtTEeTTLj7nJpa32i3CnrbpWzkosjZLE2aF7BfftIbfTZ0zY9k/74CUJHfAnicG/4u+qN0VNI31Y5NszeynPSZQhYNyvFlZ4wPPJO/GBui5lpnELmSBMv5EppkRLV8fehoZnxc9IpKw1KpOCh0zV2j7TeSK1ThoK5FDhS1tKt7sOeG4/p4MK9uU2K0t2DbdFM+8Lw+J/VumdffT6+Nvv6j9oPIliPXGio1hCAIwp3YtvS7kn437QPPvDpApXCvJK9XWX3jVz2lZhYKMlezaOrHq6MCRytw45RWf0jcxK8IonNliDNE/751X0qPdQ6YvloyNOhHL8nbIzOvfkLzAARBTgEG8OcOb1+nbuoSMK8TgmptybtcAfUNg1CXdoNGZbBLYYlUnoVDAKIaW41bn+kU/sDLPCW91ez97P7R8UsAALhxc+WDX9y/K2Vb0oWdL5n+vR4DuDE4FgOq1f5BjwFA7xh0CSR9y/p1005ZwVdWyLBX5OnJHv1LLks14sVvAQDYQecxVNQ7r9JDUXZ6R/3TYTRnLh8FUBqttuzYLh0CANwEeH7nb3ESjSBnTKbDcjT586OZoJm2gghds2vxtnCBo9mmI8dTP2qLkcDRbFujxGj7vpCjxMpNs/uxmyXz6mb6CkDdTmTsBszrxDfIc1HH7WZFBhzMGx0b17pTynp2prKhyoam5unTp7Ochlww3vzWKnz+laMPn/yVfc43gyyCsV0tJb0Vx/Pl4jmqb6fXzEJB5iXqnYMj6AgfeLH8MGqKOeJelLQlYgC5vnTZJkfFQyUXnB7Uz8sLaua5gQH8uZPIHM842lSkyoC4PIKPxtgAAF6vWQ9a8TrnSrItxbx68yiJul8+7+3sBXdzc1xSe1cCWJHgCPoPeFhP5IqU2oEHYAe7qegdAIa9Md5w0gcP/A9yzzGvbrrVzbsAkVav/byRrFAHjkZFbFA+yl9AhiHfNQNHs+lrE3ySY0Dpny8ox9cC5m27ITHaYs4pUtdT/eOSSaCypgJN7azEI9m8TT6ZHAwBJGFnFM9n8zbJxVaXzKsXbB1NwEhPKd5BRMiNyHYVybAkniFD6RCCINcQNuifaJN2oniO6NscNLNQkCX5NYCe2wxV7gMv4vcTVyF5laa45HBQ2C4vHAxBHj+U3fTJXBBBkDMAA/jFkbJwyu78nARPcFKtrQavXffqpsur6PWWv+rVzSQxigEAhCEkM1GWvxCoVruhpNJJJzSz411V1E3+OgU/con7CpSOiu5RZWg6cgAAIABJREFUNhW5Y9j1BEHOnRH38/yEcbxTUHakaK6brFqms4SqpScFjumGqtWaYQIottKTnax0mbxoISL2w5hXNzVXrBKUDE3LG2+8Mf1JyMXj27Ej+J/4epJtu0GMdks/UTxH9G0OmlksyMp9S9VsKrxFIOhmVgHGviUzKl8vP3BSTn1B/HJdYlAzzwsM4BdGKn6PlmGn6fcR9nf3vMHa6qoykrk0wrhWJuJMxlgQHAy6gw6lEzY6ZZFAz6fLlQjf5/PrgRoxZ1COrwFs0M/1M+JU53T91KxXtfxGJVd5PgK1u4ZBYNYeckrDt0Cz+dshRrttbJsduSIqmEjSYEO0itr1NpTVg7FDuA9/Xfnu9qeFXZSxA8h1IVNmBACgNHjietI+44zEc1rNFPAeSd2goShiF790kha3jk+C7YpMio4kcqVsqOSCyHUCNfPcwAB+QfBK+Jwz84QRPN9foiENQ+q6AEBUY3NDH9sppCT9K1M1P+mtB862TcN0s5VTEXWiQrW/mKAcX11Sq4hyFQDOatIVODbNdL5j5ccDEGND14eD1D76tMTzbH4DIVQ3JYBgtC6zIhOgUXZo8dCElh4Iglw1csmCBUhnIZ7Ta2Z8O9H2f2XQCYmxVTJL42sEhcF25h2zQT+15V8yVHJBBEHmCwbwi4Enr49AJ9ztURp+e83b23X5jnlIXZsOpk/15NvoQFS1Jq/JcqWiDPcKTJkyN84j/rxAZ3uccFItTMpGmdcJASC3fE15Xxf8FUCQOfD1V5BfLgQg6p0wzgIaqRuPeijN6zuYtiTm+lctPvC4xwCW/igBSA1LpZPUFv3+dwBf8Q6fManFSaLeCcVGFJ+Ndz/WYk9MdV0NgdTE5tJv/unvNJv7KxGjvTngQ8iVptwgMzaBLzkGubpEvS0/h/5OareDGM1a9PdZiefEmpm7Yx7Be3I/JLWtsXcQuxOL5IG4Woiff/QPmhs5D62sABfCkqH8BXH+dpVBzbwI/Nmib+B6Imw62r7vWyoAqJb4K+wcMAAREIeDYXIKb8OUICl6o+X7vt9uW4YKALTLi8ejuewYjnsMgPWOQSydqpbfajR0XVEUSWKDftltBx9rYkocuqZWd4JkTbgiEwj/ee/jusap1z/u/ibe/C8fHcefygaRc+S725+mJTj3ELkkLK/7Ke6RkB7C8rqIjSV9U4XQ3fbEl5r3fx/vIzwVFZlA6O6J9ha8fcZEKBsGAWo7pY0xgl89zFlkBB9pmum+MNq+7/tt9Qk9hFvf53NTfXMFQnp46574FG7RfQorm7oEkv7eMgyPHt9u+r7v+9abrjnHjwC5iLxy9GE6vSj3MA0XPdS964jSaN6+QXdcJqSBi4rbdEOo1FalsxHPmTUTxP1Q1w3TKwA5mGcLX7u2z6ef1NaE0Er6e8vQ60U/GOvLvR4sv6dLpUPMa7ohwB2Lj1hV19TKhRu5jKBmXhxwB34RpPyJgi6FKHd+TQWaWcalXbEhH69s8tN5qyVrS1ckAEmqyDIBAB4NH3/7AuBPTwvNSYug3WBD4c3wAof/RozJFgs+ijetAAAgpLYZrzHzQjEaHxCGhyHAsujrUjqqt3y9IJWfGCe2XkHOhHHrpijEl5zjfU3bTz1W11W6v+8E7/MYXrRgj1NhZmzfVoQoJ48yAIjRtqqmPYlFsGjobDtrxTeT7SglYIMXADfgcZTWo66rdP+Rx+7qEh9bWvpmR9N2AABgaQmevRgwUMD74hgqt986aoou9HAT4M3yRUYEQa46gdM8ekmMe1U3lgYAuKOSQxrljs9fPGfXTIB8c/sC2MEjvj+Uqd2nLWdjtyEx74tjWFlJfjBWVqD3hcfe18cPrT76v48BAA7t1GdE7Z/J7c9wIocgZwEG8Asg6FKRHMU7KhES8tz5NRUo5QmjXH+pa1KurURVCRUTVd6kxLUzHU0yPVCfU1PrG+1WQW+7lI2ctFojbkhtszRpXsB++Ulv9NnQNT+S/fsKQEV+C+Bxbvi76I/yUZ6REP3kBY5m0zk2REUmJreSyh9i3H4FePsdgOPRSSWTv6Dj+sTl8mOIXEmC5Xx2ZDKiWr4+dDQzPi56RaVhqVQcFLrmrtH2G8kVqnBQ18SGkFVLHDPFaueYZJ2ofcYWbJtufEZUn5N6t8zr76fXRl//UftBZMsRt4biZ/3NJ580k8ubbiYxdnLQzPLigwaZyCRwJ7Yt/a6k322knmdeHaBSuFeS16usvvGrnlIzCwU5sqPnUz9eHSUmVDntl1Z/SNzErwiic2WIM0T/vnVfSo91Dpi+On4o7zTMbYb/drZ5HOrnxQQ180KBKfTnDm9fp27qkohbN7dqhOfOKxsG4bnwzNulsET4jwBRjXbrB//O4tR4SW81by/fiK544+bKvXZLl4B5da159JI/G3a+ZDxh/sng2OPJ607AUin0IOlb1p3l9M1VVkgudT+660f/8mzkSQAAePFbAAB20HkMFfVO3KKU3FEr8FhUBJSPCgsr3OpCkPMlU6oTTf54iqgBrln3kiKZ0DW7ayI5Ug1dM5UcSW0xwtMwtV2Zp64bJMkqFTWhUQKqAZkrAHU7NT7Y0tM6wHNRLb94OU/SW/6YobF6IrJdo9RUng0VJ5qiCiEIkibo0mLPNklvNXQpvQ44TjxH9e30mlkoyNIqn0weebtUeLDz5YdRO3ZJb/mZoJ67v8fvM/OOeZs+OHkohnnbbog28AhyhuAO/LmTuL4Fe9xIXaoMiMt3eqIxNgCA12vWg1a8zrmSbEsxr948Oo6v+PJ5b2cvuJvbXCO1dyWAFQmOoP+Ap6cTuSKlduAB2MHu4XH6pGFvjDeclF9ehWiFdfMuQLR7tfbzRrJCHTgaFR2cy0dFH1MYOnWThgBA1NPoPq7dzgaurV5rxIQrMoMQqeup/nHJZExZU4GmcjnjkWzeJu93NBgCSMLOKA618zbJxVaXzKsXbB1NwEhPKd5BRMiNyHYViZ7EaPv8sPKzpgQdFi8BaJCJnAgb9E9c2DtRPEf0bQ6aWSjIkvwaQM9thqrFZY3H72Mr4ZN3ULfjQJ+H8iPHhJFVR/FQrJJRYsBprIXwC3hBQc28SOAO/OJILJxym0InwROcVKsdrcyKbXuQ9BZ/pFrpjakwFCvD+b0q3tc0vlD6UhPchbfthlEL66Ifudh95YRR8XtBXTtyoQ8ptj9BkHNlOAgzTY/5Zk5KDIr2oIpGiua6yW4P48lAIx0vRk8KHNMNC1cTTyTXUypw0mXygaNpZrQf5rdrHVPjYlN6FnL1GFcchEVDSAmBo6Woe2wC8RzRtzloZrEgK/d5X2Q+MYOgO0k3Pe5HNK+6RaWRdLHL5CEglx7UzAsF7sAvjJQFc7QMO5kNPCfs7+55g7XVVSXZ0h/HOAEXZzLGguBg0B10KA2LjhuFRYo/jy5XvPd93JUPWOCYNp3UUm8EXAicEVxbvcawQT/Xz4hTndP1U83mVMtvVOLK8zFQu2sYBMYV6J+E0vAt0Gz+dojRbhvbZkeuiAomYmxF1xStovjLjD0LQZDrCe/Am9phVhq+3wBItc84I/GcVjMFvEdSN2goitjFL52k8QbCma4mFZkUHUnkStlQ8X3M2EMEQZATwQB+QfBK+Jwz84QRPPclpSENQ+q6AEBUY3NDH9ubtCT9q6D7+8m3HjjbNg3TzVZOR34FQhK9W1D6z5Xvbn9aaAeCa6tXl9QqolwFgLNy7g0cm2Y63524LUOMDV0fDlzb3fZWZ7qpeJ7NbyCE6qYEEAxCADWzd1WRSTJBLz4LuaKgXzFyEnGy4NgZlnQW4jm9Zsa3EyXbVwadkBhbJbM0vkZQ6Nieece8yjGSzZKhzH3klz6QqwBq5sUBA/jFwJPXR5h011lp+O01b2/X5TvmIXVtOpi+VpRvowNR1Zq8JsuVijLcKzBlytw4j/jzii/JVYB+9tBhMlUuHy1gzDovgiCz8PVXkF8uBCDqnTDOAhqpAI96KM1rVppOMeX6Vy0+8LjHAJb+KAFIDUuldqaWtJjf/453+EwflVqcJOqdUGxEVWQC8Jt/+jvN5p37iNHeHIRAatEiZ9qXbhlO2r5CrgLj5qA4SUWiDRN373Po76R2O4jRrEV/n5V4TqyZuTvmEbwn90NS2xp7B7E7sUgeiNuN8POP/kFzI+ehlRXgGlkyxHsiRdcIHM3+1yVsC3olQWG8IGAN/EIQNh1t3oSU16z7lgq8GT3kukMDQNRQKUFS9EbL932/3bYMFZLS9WguO4ZUF3o26AOolt9qNHRdURRJ4snsYwk+juqwQtfU6k6QrAlXZALhP+99XI8qw+ofd38Tq3fpKPPqmlb/5eeekwz/w1FYVnSLnA3f3f40rci5h8glZ3ndT3GPhPQQltdFbJyrABf930+snpyMikxSTT54+4yJUDYMAtQub4gR/OphziIj+EjTTPcFb/3RVp/QQ7j1fT431d9bhuHR49tNXqf5pmvG7zNwNI07Lvm+3zaW4RjgxfRvFrn8vHL0YTodKfcQuV4ojebtG3THZUI1uKi4TTeESm1VOhvxnFkzQdwPdd0wU5ifhXm28LVrJ93whdBK+nvL0OtFPxjry70eLL+nS6VD6bp/3kDk2bNKUqyEXAVQGC8UuAO/CLh1kbEqiRxWkTu/pgLNLOPSrtiQj5dK+emOZtO4ZFySKrJMAIDHysffvgD409NCc9IiaDfYUKLSc/4bMSZbLPjIPkw/DqltxmvMPFuKxgeE4WEIsCy6n5aO8jXdhzvJz1MY9gBgZQ33vhbCaYJ2XJq9wBzva9p+6rG6rtL9fSd4n8fwojt7XMk5S//3MYhK8ygDgBhtq2rapWmp8ZlbRsd0bWet+GbSG+YxbPAC4AY8jqpS1XWV7j/y2F1dYt4Xx1C5/dZRU3Shh5sAb0brjBWAYZyooFrWO7a967GZWnEgCHI1CJzm0Uti3Ku6sWoAwB2VHNJol2H+4jm7ZgLkm9sXwA4e8f2hTO0+bTkbuw2ukisryQ/Gygr0vvDY+3rZkKS3fNnRko9o6ccWSieCnBkYwC+AoEtFchTvqERIyHPn11SglCeMcv2lrkm5thJVJVRMVHlzENfOdDTJOIU8p6bWN9qtgt52KRs5abVG3JDa5iTNltkvP+mNPhu65keyf18BqMhvATzODX8X/VE6KsmvFb3i7yb8qUIuBLmFWP4Qw/gLwtvvAByPTiqZ/AUd1ycul/tI5EoSLOdLaJIR1fL1oaOZ8XHRK4rGFgAAELrmrtH2G8kVqnBQ18SGkFVLHDPFaueYREzm1W1KjPYWbJtufEZUn5N6t8zr7/O1UT70N5980kyuYfJOS3DQGWbfGfPymbETg2aWlxc01ETScCe2Lf2upN9tpJ5nXh2gUrhXkterrL7xq55SMwsFObKj51M/Xh0VOFqBG6e0+kPiJhnvEJ0rQ5wh+vet+1J6rHPA9NXxQ7roIdKW62anZlRdt/8XE37Co6B+XkBQGC8amEJ/7vD2deqmLnGdVDe3aoTnzisbkY0b83YpLBH+I0BUo936wb+zODVe0lvN28s3oiveuLlyr93SJWBeXWseveTPhp0vGU+YfzI45j4kmhOwVAo9SPqWdWc5fXOVFZJL3Y/u+tG/PBt5EgAAXvwWAIAddB5DRb0Tl66TO2oFHouKgPLRoNsDqNyORwkxrLbvf4KLtwhypmRKdaLJn3CUBNdMewCFrtld42mTlhq6ZiqrndpihKdhartyW5hSpk3ZNLtvRIaVbQMyVwDqRsZuLT099eW5qNaIA6a4fb01ao4pKKm9LCvLzNTt8OZUCIJcX4IuLa7nk/RWQ5f48+XiOapvp9fMQkGWVvlk8sjbpcLUnS8/bIykAyQ2dQLu/h6/zxIlLBNJ5m27YGzp8vgPFEGQuYA78OdO0nM92HNDUDcVqTIgLl/DjMbYAABer1kPWvE650qyLcW8evPoOL7iy+e9nb3gbm5zjdTelQBWJDiC/gNePkXkipTagQdgB7uHx+mThr0x3nDSBw/8D3LP8aYlm3cBoj2vtZ83khXqwNGoaEFaOso7Yf+0Oa90XVy7PX9wafbSw7xtNyRGW4TDInU91T8umQQqayrQVC5nPJLN2+S1MYMhgCTsjOJQO+6THJtuFCV7Mq9esHU0ASM9pXgHkcGwdAgGhU2iwpnaKKPz4iUGDTWRGDbon9iK7UTxHNG3OWhmoSBL8msAPbcZqpavSxDF72Mr4ZN3ULfjQH84XgnlcpHk4Xtbl6C0a8nJ4BftIoLCeMHAHfjFkVg4iRYoexNqHk9wUq12tDIrtu1B0lv8kWqlN6bCUKwM5/eqeF/T+ELpS01wF962G0YtrIt+5GL3lRNGAQCIXGH/P3vvFxrJled7fueCC8YuZlz02BqIjJk8xV73xZtgKDwKpFnRxL6YKbbjmiZKDwJH02bMrZh+SPKh9eDoh1xw1IP6Qm4+dKcaD904FrSLFLA1OeChXvrQiJUIUYjrJa/p9oAjdzLPQlZNU91NVTeUH7wP58S/zIhQKpWSStL5UA8lnchQpEDfPH9+v+83iF3s6hl/PIlEcuoMB2HG9DjjSQSUeUpmR/LmuslpD+PFQOPhlTkvClq2F+buJh7JmKcUd1Q6ckgiAVDc+CMbgiQQXpcxdZ9NIZ4T+jYHzcwXZO0e90UWDkLB3jRuejyPSHdOaJofnb7L0snLiRTGFw15An9upCKYo23Y6WLgOWF/a9sfLC8taeMx6pMUCbh4JWMsCPYHe4NdSqc0OmWR4p/82Jw73+9u2GH8s8OMP95xkXuB54Dcmr3gsEF/zM+IU53T/VNmc7rTbVT8uu2VXU/dPcsiKGrQPwqt0XVguPztEKvTsTbsXT5LLh7Kz66UOUgSydVlIs1ca3S7DSAqm8epiedxNVPAPZL2goamiVP80kkaj9vMeH8UpPgStVI2lJy+SySSs0Au4M8J3gk/lsw85Qqe55LSkIYh9TwARLfWVs1Cw7eSGWgqK3n6Rw9aGy4N02YrJyYMoTudBn8L4gdMEf8seUH4+tZPc9NE5NbsC0J+Drx1txrvIqpVAPNLfc8StFya8YcrK7AZ9RhwXV81zeHAc70Nf+nIh8rLga+oBOChl7v7n1VDVNeU0iEGxBEczK/bg7WO2pdpllcSLlwyU0OSFAuywYadnwOvnIZ4HkszM8TF9pXBbkis9ZJJ1Kd1YzMOBh5kupX6ex8Zce7Qok5SSpg7xPZ3Q4RhZhfDs40HVudjuaa/NEhhfKGQJfTnAy9en4Bu+VOptNbodhxLj/ZCQ+q5dnlUci78GB1E1y3LcZxOh6fSl8H8uu3SkFidTEF+nttT4olSOqqoVYBY6414A0LRGutWusBeIpGcnGwOfMeCt5lUAVVUYaUZE7REgeh8SJeYFuhfilf+QuEuzFP0Fk3mwCNoGYmnlFP1NmkcS1k4xA2gBsNYGHllrDyAv7J8feun/F/6mzz6WKYfXxlE142dqIZQz6YXieNpiefxNDN5Yl7A7+/vlgXB47Bpboa4rk/kwCtLKwsI6YHuRCZ5BzTEglopG1LMdtfR07nyIFanK1fvF5gircsVRsnZIxfw54KI6egInYPuiP9FHwIZd2gAkddSgqKZjXa32+12Oo6lI2ldjw6jCki50LNBH9CdbrvRME1N0xSFl7MXEnwU9WGFnm1kGtUrKkH4i+2P6lFnWP2jvX+ND/9LR1/63TWEnp3uK+M/RnjlSy4EY4Iu9f1FZ0xRxprDhf/7kd2T01FRSWohzu0zpkJbtQioW7w7GbSM5DAogrXaFCDv8IcXqvbbASsf4r8C6hqicPXxP83xNyC5+IzNZeUy/grDDyWe43p1STkd8ZxZMxEpmeeVrd9Z678ePsfNu1s8mU5rdKxU2/7Xky/4GkcNsUFfVixdDqTWXQhkCf15EGx7oXAGDfYootr5ZR00Y5BM90QDKLcIjV/eMlxKLGfd1BRAUSqqSgDwtfLo354BX/17bjhpHnQvWNU0Bbxy3QsRV5FOPPWH2YlyplGdN4rR+IIwPAiBBeF+Wjq68FevIMq+y/DKXy3kfFcikczEaMcwdlJf605H3bLjNnOt0W3+zmzGNZDkblRROeoxAL9JejKvA68lI3/4Imi5sUARfBZnGl8TfaCK2XYGhpsq4X+d4JEQmi8+B571HtS9+zwn+e6N1DMq6mtASN36N7vt2+NvKMqB/26v3jxMfX+Ip8D1cNMwNvkbvWthk+d8lAwp6dp6AL3eLA74EonkUsGN3DttddtIF4jrd63+r7HKvygWzxx9O7FmFgpykgOfmNvn58CzB//tKYAvYyUU3/6Xw4a2sL/7CERfTHquFnVyQEUOfNGQqQwHIfQ1qZgSydkgF/DnQLBHRXEU87coCCEh3fJXNa65vPWbr+apZ1P+gUB0nVChmNykxHMzjiaZpJCn1Db6Vqed422XipFTllaIF1LXnsaHmf3sJ73J74ae/aHavacBFfV14NHYcLxXWzaqvP9J588mW/Gv6/fel1u5F4exDVr+pTyEf0F4401glLMeDfYSgybm15uHitiSY37d3qz7b7VNRYhGuLm3xt2bgpbh0r9RFUQjO3t/3+02xEDoPVjpdLuKmEyKCvRoEixOvv267UURSW+8CdDDz77T6bbF4G6cmBm03AMUrqMj/06GJGMzgnwn1ZLK/PpUQ/EN/brtVU9i0SnDLC8ZMinzqsLthlUlca9LiPcUi8UzX99OppnFgry0IiKJVYjuqIIc+Mlg4KBluPRv/+6W6LavLv+wnY397UdfFAyxQR8EW4Yh/hymSK8rROrnOSK17qIgS+jPHG5fp6+ZCq+k19fWV0QDlbYaxbgxf4viBuE6TnSr0/7W71hcGq+Y7eathWvRHa9dr93ttE0FzK8bzegwO9z9jPEDsseDEc8hMVoBS5XQQzHXncXMKXelRsZK96OnfvDL8TZTwbPfAADb332Eir4YW5SSRb2CR6IjoHwUUMx2x9FJyt+0Yv2oIZfvEslZIaKMowwgxVy3SKb9PBs7nHaoyGQaJ6EXSU8517NUSel4ytIp5MBnWlKHg1DoWsmQRCKRjHGMHPhi8SzPgZ9NM3MFmRfz7z742Rbl48EeRVkjfPwOxnPgJwkHw7IhDAchwupabBOwsmsbM1gzSSSS6ZAn8GdOkvoWbHsh9DVNqQzErqkZjbEBgFdXnE/a8UFRLTlkYn69eZj0hz9/2tvcDm6PTXPJylsKUFNwiP4n/HibqBUldQIPsP2tg0yf+bBXkA03uV8bHVOt3QaE5OvLP2xkN2bF4V75KP8BWqOtNZL+gNnbx+Te7dkjt2wvPMNBmD0yib2MRTDGSXPgueilcy+qpS8SOfDtGXPgU/714znwBUNzRaYnXjZkUqYEQCbdDeBH5EeK54w58KWamS/IvCnz/v2a0zVFj6Z+5GxqPjnwYxUKXGpnzAGVf1PnidS6C4JcwJ8fqSB4PqmcNjaNW+DpjjBuZ37d9ngXvdnuLvHiT3FqxQAgDGF1ukJE2fiNUvlt6Vsd/RT+hhd3POXtUkfpK9BQOpoW91R5gkQiOTNkDrxEUoJMyryiXLAceK3R7ah12xMt8WeYA59PmPrNSS4GUusuCnIBf26k1u+8BopOuXLmhP2tbX+wvLSkJTu1RRQtiMUrGWNBsD/YG+xSOqXRKYu2bOfr8STc/Sb6tY6F3CM8B+SW7YUkJUIvTg48AIBYx8mBzyNzHhS00jnwJUMSiUSSIv+sIc3FzYHnewQk79Ez7zjrL18yJJFIzgy5gD8n+FEzaMpjNDKjP/K1vDSJhjQMqecBILq1tmoWfryU9G+li7OmfvSgteHSEMTqzNmimS8n5PH7xUNu2b7gfPE5xtUGIPpimMmBz2RgIGgZbn9+s9LJTONq/oWjHgNu/FEBlIaj02lKk6LszPRVKW0j+mKYPoiabih0W8vShV4Sw9Vs0q1TqtylRnTdbH+K/mZqskSs5kr0/9MSz6k1c+yJ+QreV/shWVkvfII43EgUD8R2I/z1hz82vMi4uFYDyEqldIi3VN7R6U70KXNzITmjklwoxrQu90vJuSNN7M4HLsYT0C1/qm1WrdHtOJYeFTOF1HPtGdxC+DE6iK5bluM4nQ5PpS+D+XXbpSGxOt30ZxPfgs4yHIRiZ7Z8NHmcdE2CRCKZLwt3uinukpAeYOGOWKJe0Bx4AAh+fn/MYTP40DBs75nV6Xa73Y7+mB7g5je1o4aSatKORYAFUFeaMEnGmJy8ypDkS47WaN66Rjc9dqsZO7Tpj72mF6KycmFz4H3X47l2nW6323X0lNwp5tsL6PWiD4w7C70eFt7mgcGFQ8rSygLov6AjfkPv3vhylGir5ALy9a2f8n8yFv7FRJ7Anwu8i93qtE0lCekMWoYb7eJO9F2JBW9yC0UzG5rZABgL9rddLy7AzzuMSsETRHsjmAob9JGNaGL7/bLHDj4yRBR86NnGbtyFD7EF/Yvtj3bpAffLI4vVr+LD//JRAGCB/+O2qEno69Z6Qx7DXzDkHu0Lz0QO/B2d7uy0gve4AojuybiTc44p6GOZxsTqOFXbLS1LjV+5bu3aXuFheNZRSsAGz4BreBR1pep3dLrzwGe3TaVkSLiKOm1TYT6AN+9aL296x+lrkkgkl4+g1Tx8Tqy7Va9pGPF3F3VyQKMziPmL5+yaCURNmSV7CGz/AU/eyPTu03ZrdauhMP/hCLVa8oFRq6H30GfvmWVDw8EIrxNqZxLifx3gttRPieQ0kAv480C0ei8pkWcoX3wv66CZOiy6Jxyc4lon/nJh1L5uagqgKBVVJYAIDx392zPgq39n0xqH0L1gVeNmeEGLb/IWfEYEH4rVuyCkrh0XifEdBxpfEIYHIbAgjFnLR4GgZafeX0g9mw7mt3qQzJmSVbpct7+YFOTAM/UhLfKJG+u8IWolWSyPN00mI7rTNYe6DwEtAAAgAElEQVQtw46vi36i1nB0Ki4KPXvL6nRTQZFV7NcNcSDkrKRT3YNtLyxqA2J+3aXE6qxjw/ZSOfA89SL1bpnf3+HSWjKEQT+qAOLuIMyvH9H8WozMwrisyMSNqwZPUl83byvm7XQOPPPrQCV3qjWuV1l943c9oWbmCjL31bM6bROIxCw5I0o/kbL0DvESu2NEr1URHzB9v31PSY/t7jNzqXhI5b+mbNdA+vd1HKR+vjhIxXthkQv4cyDYo6K7iflbFISQkG75q5q5rINS3vHJV/PUsynfHCW6TqhY42qrFqGe52YsSTMhJk+pbfQjFc+SipFTllaIF1LXniZIif3sJ73J74ae/aHavacBFfV14NHY8NfRf0pHeZTT6wt4xJcYLPA33OIzN8n5Mdn/Cblov8BkKn2iyV83mgnadSTr9NCz9xzu/Ra0DNduqfGfJ3XFSNAyXNegxOp0uwq/X5LXZrj9OAyD+XU7fQdQb9fqdNticDd+QF6LWrSZF/l3Mn9yrMT5o2Bowg2UL/alPZNEcoXhrX05MqCY7Xh9Wi6ek/p2cs3MFWTeor67f4hdSqyOhmj7YdIYuETuGDDW36ioVaAffZU7FJnZjXnnyymcRHJayAX8mZNKSmP+bgjdWVe3+BamuWoR6tG9oFEZbFHcIJUn4RAgurXeuPmxSfEHXhqvmO1m74N7h6PnAIBr12vv/+jebSVrSRfufsbMv+wx4NpgJAZ0p/OtpIQeirnu/KrppqLgKzUy7OUFf7AHvxxrM4149hsAYPu7j1DRF18SRfIgi/pXB1E9Qekoj6B7dxn37/OZtaLxsllZvCqRnBXM3/BCYnXEnFOUrqf845JJoLasg6ZOpuORbN0mn0wOhoAi4ozi+ex4THK+dyXz6zlHR1Mw4Sk1HIQABsPSoezP51VPM0diyPCFS4tM3LhS5EXkTlxzlHhO6NscNDNXkBX1FaDnNUOd58CL9XthJ3zyDlJyN96vKQgHQ6glQ+AdkHwrgu84GK0Z1/DyT+kFQirei4pcwJ85ycYnb7lc05TKgHh8BR+NsQGAV1ecT9pxoVItqStlfr15mKy6nz/tbW4Ht8d0kqy8pQA1BYfof8KX9UStKKkTeIDtb6VW7wCGvYJsOOX9T7rvj32Pu46u3Qai3dvlHzaSkqmgZVBxuFc+CgD4s3fa4z9AFq++WMhKqsvMcBBmJ3rjE8bipKDsSN5cNxG99CZjtfRFQcv2Qt1pzzD9E77RyUlW0qBTMjTxw3k//PF/vOQyIxM3rjhZ2w1iddrmkeI5oW9z0Mx8QdbuObrh0sgMONjL7AIUvqV5yR2x1qN7cKktaNCSXByk4r2wyAX8+ZEyXRc5JUemJXF4h1JsIcf8uu3xLnqz3V3y63bS2cQAIAwR12FlwkS5F77udCIvutStjn4Kf4PvPwD5u9RJfCpKR3kZr7fhq7ypHyzgjqsEEonkTGCD/pifEac6p/uPFVZW/LrtlV1P3T3LIihq0D8KrdF1YLj87RCr07E27F2uQCVDnNiLXq7eJZKrzoShsNbgheuibB6nJp7H1UwBb7HcCxqaJk7xS2dzOXJXUXPnXkStlA0B2dw7Tl49p0QimQdyAX9uZELTeB3VcWrGw/7Wtj9YXlrSJnqZJijyIhWvZIwFwf5gb7BL6ZRJJSzasp1HixP/wAnHmvpnRlb1nBaykuqykRIhtQrgtBatQculGee7I9MyibVqmsNB6rD8uMTzbP4AIaprytFDfNIsV++SEmTixlUiddZQcIVyGuJ5fM2MHyc6/q8MdkNirZfM0UrkLvOOowb3kiEF1VSbvORSIRXvxUQu4M8J3gkPGoWEAIjM6I98LS9NoiENQ+p5AIhura2ahR8vJf1bma75aR89aG24NEy7pZ4QxWx3VH5TAER31lcHG/NZzUvmh6ykurh88TnG1QYg+mIYbyJONIdHHkrzmpWmD2d47U81/8JRjwE3/qgASsPR6TSlSXnZmSltI/pimD6IKhqK0z5CzzY8uYqXSCSi62b7U/Q3U5MlYjVXov+flnhOrZljT8xX8L7aD8nKeuETZOUu5TnHX3/4Y8OLjItrNYCsVEqHRr+7lleG8Hjqdyt5wZlhpifX/KfKfzjvB7iicDGegG75U22zao1ux7H0qJgppJ5rt4JjPwU/RgfRdctyHKfT6Tr6ES9hft12aUisTjf92cS3oLNwJxRVOWqU30BrtLucdkNTxkYlEsmJWbjTTXGXhPQAC3fE2lgx13SE3kakQNz/vThH+FhUVILQ2xYSxbtvpkJbtQioWy5uwc/vjzlsBh8ahu09szrdbrfb0R/TA9z8pnbEEPObXggsOvz341Q925hBVSWXnj85/C/prcyxLyWXDa3RvHWNbnrsVjNSz47+2Gt6ISorS8rpiOfMmgnxPNTzwsmS9hjmuyLXrtPtdruODupGeqeYby+g14s+MO4s9HpYeJsHBhcO3Wq2LQLoTvQBcxPAjXe/L42IryRSJM8AeQJ/LoiczU7bVJKQzqBluNEu7kTf1YQxqKKZDc1sAIwF+9uuFxfg5x1GpRglLvRs0B9L+mD7/bLHDj4yRBR86NnGbtyFD7EF/Yvtj3aFzzwhi9Wv4sP/zAZ10DJc3LX62dKA6FwfACG1VwqjnyXniaykusiMdgxjJ/W1fkenOzut4D2uAFqj21HrdnyEMscQIMVsOwPDjSoAiNVxqrY7jUulMHQuDJXMOkoJ2OAZcA2PouMg/Y5Odx747LapFA/hwT+PAODANYz4TtT9QO18LM/hJZKrS9BqHj4n1t2q10xpw6JODmh0yjB/8ZxdM4Fxc/sc2P6DIYCx3n3abq1uNRTmPxyhVks+MGo19B767D2zbAiK2e6qLSP5HVWse+9L7ZRITgm5gD8Pgm0vMi0N9iii2vllHTRTh0X3hINTXOvEX94yXEosh3u+KUpFVQkAvt4d/dsz4Kt/Z9Mah9C9YFUT3nEtvslb8BkRfChW74KQunZcJMZ3HGh8QRgehMCCMGZNfOq+iy2XAgv/lzeKbVvH6/jDsAdgoXjvWHK+xOt2uZK/KLzxJjCanFQy9SEt8okb20AjaiVZLI9XlycjutM1hy3Djq+LfqLWcHQqLgo9e8vqdBvJHarYrxviQMhZSQI3hFgW7OYxv+5SYnXWsWF78SuiTIzUu2V+f4dLa8nQeNAGT9n4+9lW7zIL47IiwziuGjyJbd28rZi3G6nvM78OVHKnWuN6ldU3ftcTamauIEdx9G0TiLqjkjOi9BMpS+8QL7E7RvRaFfEB0/fb95T02O4+M5eKh7hQcn8R8RxH2t8XIvXzQiNF8myQC/hzINijoruJ+VsUhJCQbvmrmrmsg1Le8clX89SzKd8cJbpOqFjDc9O3Mc+3TIjJU2ob/UjFs6Ri5JSlFeKF1LVzg5TGYD/7SW/yu6Fnf6h272lARX0deDQ2/HX0H/FJ5LlN/vVolLLWU27euIbw+dhrX5rioSTnxVg1FP9SLuMvGplKn2jy141mgnYdyTo99Ow9h3u/BS3DtVtqPPWjrhgJWobrGpTwJGDm1+1UXpvh9uMwDObX7fQdQL1dq9Nti8Hd+AF5LWrRaVbk38n8ybGSAp6pantEtHOZA5REIrn0cKfPnH4+xWzH6/ly8ZzUt5NrZq4g8xb13f1D7FJidTRE2w+rEzo2YX7M9zZVRdjlZToYFbWacqgrGRK/sm0vnCK9TiKRnAS5gD9zuH2dvmYqYP5uCN1ZV7fEFuaqRahH94JGZbBFcYNUnoRDgOjWeuPmxybFH3hpvGK2m70P7h2O+KL32vXa+z+6d1vJHmWHu58x8y97DLg2GIkB3el8Kymhh2KuO79quqko+EqNDHt5wR/swS/H2kwjnv0GANj+7iNU9MWXRAk9yKL+1UHW1yXF9Zrzo2Tn98snz/E6qTwKhwBAiP7OW/3N+0WvlUgkc0csWDvRrhovXU/5xyWTQG1ZB03V6cQj2bpNPpkcDAFFxBnFf87jMcn5SRnMr+ccHU3BhKfUcBACGAxLh+KfHx1sJb+N4yNzGS4tMozjSpEXkTtxzVHiOaFvc9DMXEFW1FeAntcMdYcv/Pn6/chqRl7kKW453q8pCAdDqMVDKQFNJyzNhvxTuthIkTwT5AL+zEk2Pvk25ZqmVAbE4yv4aIwNALy64nzSjguVakldKfPrzcNk1f38aW9zO7g9Ns0lK28pQE3BIfqf8GU9UStK6gQeYPtbqdU7gGGvIBtOGa8vRVRiunYbiHZvl3/YSErMgpZBU238qsibD1qGS//mP6dq9Plr137yj1rqtZv36YwBorL46rSR9VGXkOEgzE70xieMxZ6S2ZG8uW4ieulNxmrpi4KW7YW6055hGih8o5OTrKQBqWQoRqTMBS3DNnalFb0kgwzjuOJkbTeI1WmbR4rnhL7NQTPzBVm75+iGS6Plc7BHpzgKj1KB5yJ1vK4058xfcmWQInk2yAX8+ZHaphQ5JUemJXF4h1JsIcf8uu3xLnqz3V3y63bS2cQAIAwR12FlwkS5F75YWCN7q6Ofwt/g+w9A/i51Nj5VMxt5d5nqtRKJ5HRhgz7yQoCqc7p/atarO91Gxa+X50RSd8+yCIoa9I9Ca3QdGC5/O8TqdKwNe5drTMnQ2D1WLUI9WQgkkVxhJgyFxf5eVDaPUxPP42qmgOvWXtDQNHGKXzqb49sDGVeTikryriRqpWwoYmI3QyKRnApyAX9uZMqMeB3VdCtnTtjf2vYHy0tL2kQv0wRFXqTilYyxINgf7A12KZ0yqYRFW7bzsqmeI7JE59SR9VEXjfwceOtuNRYhtQrgtI6bg5ZLM853ZWmZox4DruurpjkcpA7Ly8iL3qioBAgBINzd/6waorqmHD2UemTD/e9kvDhUIpFhHFeL6DSBDTbs/Bx45TTE81iamSE+/q8Mdo+w8fi0bmxyIfRsY5DpVurvxaFDwKJOUkf+JUPM/3uXAvBk8dIVR4rkGSBz4M8J3gkP6hocV5jRT/NaxVzTgTCknufatmEY9ZYflKh7Sf8W8+uGYdu263retKt3FrTqNv9seQGX75LTp0iLpUa/2GRz4DsWvM1kE7GiEoS7+ykdCVqGUfennjYeRTqSmNf+lPHKXyjc+zKVhVzEZA48gpbBPaVEovsmRS0qKi0aYn7diIKQeWn9V7+VaZaSIr6+9dN4kiojji8vIuTdTlRDqGfTi8TxtMTzeJqZPPHSCgHd8/d3y4Lgcdg0N0Nc1ydy4JWllQWE9CCKdHf0AxpiQa2UDglp/eoGz4J3qp4dyankSiJX76eNXMCfDwViTLemk3yt0e04lh4VM4XUc+0ZpJIfo4PoumU5jtPpdB39iJcwv267NCRWp5veXeVb0Fm4E0ph4+xcXiuRSGaDu7fFiGnqRqRA3P+9OEf4WFRUklqI8+6bqdBWLQLqFotb0DKMbLwlANZqU4C8I+ybB30A+O2AlQ+JaW+QdMY/eVKx1uUhkiSXsXW7XMZfJfis5TmuV5eU0xHPmTUT4nmo55Wt31nrvx4+x827Ww0R/9axSHKG9PXkC75G+RDvfdf/4xO+6ak1HH3qGa3kciG18WyQJfTngsjZ7LRNJQnpDFqGG/kjT/RdTRiDKprZ0MwGwFiwv+16cQF+XjVpilHiQs8G/WwkMth+v+yxg7hwKvRsYzfuwocwd/7F9ke7woWekMXqV/nHV48x9oBjxtBBy3DpdXn09UIj66MuIKMdw9hJfa07HXXLjtvMtUa3+TuzGXdykruRNHDR+E3Sk3kdeC0Z+cMXQcuNfZ0IPoszja8JzVLMtjMw3FQJ/+sEj4TJxRefA896D+refZ6TfPdG6hkV9TUgpG79m9327fE3FOXAf7dXbx6mvj/EU+B6uGkYm/yN3rWwybvZS4YUxWx31ZZhGNF9brwrk5AkkisON3LvtNVtI93mrt+1+r/GKv+iWDxz9O3EmlkoyEkOfGJun58Dzx78t6cAvoyVUHz7Xw4b2sL+7iMQfTHpuVrUCc8UWioeUgchsIjH8T7DROKHRCKZJ3IBfx4E215k8xHsidr5KPo9rXd0T0ytecZH/PKW4VJiOeumpgCKUlFVAoCvd0f/9gz46t/ZtH2bdC9Y1bgZXtDim7wF5nHBh9mTrpC6dj9qc+I7DjS+IAwPQmBhwsqE+R9PxMnz13obvrpuakN++PUU5DvSBuVMmWE1LtftF4U33gRGOYlswV4mB755qIi/aObX7c26/1bbVICFmoLDcHNvLYkdpn+jKohGdvb+Ps40pqH3YCXJNBayFE+Ck0xjLzJHfuNNgB5+9p10TnIUuBG03IMpcuCRRHREkO+kOjCZX59uiDtA7a502uq24fb/7Fi/5DQyC+NyI5M4rgxxDnzsXpcQ7ykWi2e+vp1MM4sFWeTAM1OF6I4qyIGfzBUKWoZL//bvbkU58Ms/bGczhfrRF8VD5D+ttn9YZFd8HKR+XlykNp4ZsoT+HAj2qOhu4kVHhPBKI21ZR1Q0pS3rAKhnG4ZhGLYHPS6Y52Wloefaon/edr0w0y2Fp9QuasBaqCmAUluAKBkFje5ju2VN8OxnP5lYeQOhZ3/Ia64q6uuTw9liKxa06rb3aPIyreHoEO8o2qiozal4V3I0st5JEkUZRxXjirlukUz7eTZ2GP0BmxjhqpXONOYecFzoUiWlSbV6xNxz4DMtqcNBKOzoSoai3wJk2bxEIuEcIwe+WDzLc+Bn08xcQebF/LsPfrZF+XiwR1HWCB+/g/Ec+EnCwbBs6AhplUgkc0aewJ853L5OXzMVMH83hO6sq1s2L+JctQj16F7QqAy2KG6QypNwCBDdWm/c/Nik+AOvPFfMdrP3wb3D0XMAwLXrtfd/dO+2kskLRbj7GTP/sseAa4ORGNCdzreSEnoo5rrzq6abioKv1Miwl+e7zB78ctwnSvDsNwDA9ncfoaIvviRK6EEW9a8OxgvjQXRH/9ylo/GbaI2OA9elsdYv/mfpjyeRnB0yBz5avnfmsXyXcQyXHJnEcVV5sXPgeU3n/fs1p2sqZ5oDXyqtx0X+EV1gpDaeFXIBf+Ykuhxs8yB1pTIQZU9mNMYGAF5dcT5px5WetaRKlPn15mGyCH7+tLe5HdweO6ciK28pQE3BIfqfcPknakVZqCk4RG2B32h/6yCzmB72CrLhJguuxOdKde02IDZb9eUfNrKVVak2flXkzQct0NGbb4zdCwow5B35aBkufaX0d1iKLL46FrLeSQKZA1+Z6/Jdctn5+tZPc8uUZFfRZeOC5cBrjW5HrdueaIk/0xz4EtWVXB2kNp4ZcgF/fqSC4PnW5XZgTlUsyi3wYgs55tdtj3fRm+3ukl+3vWpUdsoAIAxhdbpCndn4jSAW1sje6uin8Df4/gOQX2YWxafyfnrNLO2MClouFbl0MnhEIjkbUhr04uTAAwCIdZwc+DwyHatBKx32njvE/N0QYZiZhcs0Y4nkipOdyORxcXPgRWFk3qNn3jEb9NM58MVDJaorkUjmi1zAnxupuTNvYqJTrpw5YX9r2x8sLy1pyZF+EUV5JuKVjLEg2B/sDXanjYIHi2qu5pMEL5bv8/n4k1U6x0PWO10BvvgcAE05GgMA0RfDTA581jI4aBluf36z0slM42r+haMeA278UQGUhqPTaXY286I3UoWnRF8M0wdR+UNLK8QLJwQwr6FIIpFJHFcFURq+/Sn6m6koN2I1V6L/n5Z4Tq2ZY0/MV/C+2g/JynrhE8TeyKJ4ILYb4a8//LHhRb5HtRpAVirRENdE5tftwZqDkA9x0g0GCzjq+F9yOZHaeDbIBfw5wTvhx6bUU67g+QcKDWkYUs8DQHRrbdUs3B8uMWDJdM1P++hBa8OlIcSB+YmJsqDkMde5IOudrgwLd7ofvxd/9Wnd2DzAwh3xRzzWwSiijOcUo1ZRCWi8ED9mDjz13NZymZNd8PP7T4CF9Hc+NNweXueFR6y1ZlPcvKsdMZTdCRUT0Rz/ZokkIZ6qchWVsnkJ0RrNW/93c9O7dqvZbd4CALDWmt30gArvfD8F8ZxZMxGLuQdiFa7fme96PNeu0xZhc64BvoZXzLcXvJ1e9IHxv39g7IwW7vC8IXNN91y3tezA9ULU/sntRe9USCbfB2D+B7Y3wl+f4DcgebE5cn0uxfC0kQv484Hvpk5Ap+z41BrdzrK/veXxE/OQei4dHN+tmR+jg+j6irqsqpWKNtw2yp1HxIp/vOZKUatAP3spb4svdL6K78h/FWMNZNQ1aG5hl0QimY2JHPg7Ot3ZaQXvceEQ3ZPxH+Is/u8FjGUaE6vjVG23tCw1fuW6tWsXL+GzjlICNngGXMOjSFT0OzrdeeCz26ZSMpS9xxefA7jxrjSkl5Qytvspl/GXkKDVPHxOrLtVr2kY8XcXdXJAo9rx+Yvn7JoJRDWdRbWXANj+A24ZnOndp+3W6lZDYf7DEWq15AOjVkPvoc/eMxVAa3TvfG643B+n10veaUWtAMP4VEp3nDddd8tns5iYSF5kpOi9IMgF/LnAu9itTttUgpYhopKCluFGZVgTxil8OVxNbqFoZkMzGwBjwf6268UF+HnVpClGiQs9G/THPmjYfr/ssYOPDBEFH3q2sRt34UNsF/9i+6Nd4UJPyGL1q/zD/8cofcCEr466QDIfZL3TpacgB56pD2mRT9zY3y5RK8lieXxvLRnRna45bBl2fF30E7WGo1NxUejZW1an20juUMV+3RAHQs5KOtU92PbCoiqiqHhnHRu2l8qB53uHqXfL/P4O19aSISVz54cjEOve+3LqKZFcbXiS+rp5WzFvp518mF8HKiyvw2Zcr7L6xu96Qs3MFWTuq2d12iYQdUclU8z0EylL7xAvcUtC9FoV8fz0++17SnpMqCTz6zujWHWTLkq2vzvMfjIwf7yzQCKRzA+5gD8Pgm0vSh3hoR988b2sg2bkju6JqXXcrMRf3jJcSixn3dQUQFEqqkoA8I+M0b89A77699xPlTzoXrCqcTO8oMWrtAo2eYMPxepdEFLXjru8+I4DjS8Iw4MQWLDG80eZ//FYnLxitrtmTik/seZUviuZjvS6XS7mrwaZjcJo8teNZoJ2HclsLPTsPYcbFAUtw7Vbajz1o64Y4WWYlFidblekWiahQobbj700mV+303cA9XatTrctBnfjBxS1qAWnWVHRO/Mnx0oah44IdUYUEHISAZJZGFcBGeFxBeBuRTmlhIrZjtfz5eI5qW8n18xcQeYt6rv7h9ilxOpoiLYfJhuBJryTkpJJBoyl1KVLLMtUt/hVx0Tq5wuLFL0Xh/9w3g9wFQn2qLAnYf4WBSEEdMtn0JZ1IPS2A/AKKIB6tmEYhmF70PU4vkNbtQhCz+VjhmG7XpixO8FTaht1P9e1dKGmAEptAdyPBKDRfWy3zMKO/ewnvcnvhp79IXeNr6ivTw5/nb1F0Krb3qO8m+/vhtCdbrfb7XYdHThpJKlkVuJmztwvJZcW5m94IbGiknHFXLeIECNOMgnUlnW+zTc+wlUrqttUllYI94ATcUZJPaeytEJA91KBE3nFnsyv5xwdTUFFJQh39xMBHA5CYUdXMpSQMRiVSCRXl7yEnYlrjhLPCX2bg2bmCrKivgKEXtOrihlUsEezk8Oid1B344X+cJA7ExxTyXFme5VEIpkVeQJ/5nD7On3NFMFFurOubonypFWLUI/uBY3KYIviBqk8CYcA0a31xs2PTYo/8MpzxWw3ex/cOxw9BwBcu157/0f3bitZS7pw9zNm/mWPAdcGIzGgO51vJSX0UMx151dNNxUFX6mRYS/Pd5k9+OWT/Pfz7DcAwPZ3H6GiL74kSuhBFvWvDlL1BCKvRHf0z106GrvHcJAxMpVIJGfMcBBVBQniMCLhq1kttLPIjuTNdZPTnrRGVUtfFLRsL9Sd9gzL6DFDvqCV1C+VDEXwbdUTmtfJBIcrgYzwuHpkbTeI1WmbR4rnhL7NQTPzBVm75+iGS6Ptx2BvGje9KFToxTk1kX8+Ly5S9F4Y5AL+zEl0mddprmlKZUA8voKPxtgAwKsrziftuJ2oljSGMr/ePEwWwc+f9ja3g9tj51Rk5S0FqCk4RP8TLv9ErSgLNQWHqHHLZra/dZBZTA97BdlwyvufdN8f+x7z67ZXXbsNROVXyz9sJD1iQcugqTZ+VeTNBy3Q0ZtvZG406ANVDFt1m4YAiH6S2bMsvpoZWRx1ZWGD/pifEac6p/unZr26021U/LrtlV1P3T3LIihq0D8KrdF1YLj87RCr07E27F0+Sy4Z4kzMxiWSfGSEx+Vnwo8ojjpnkYidkngeVzMFPLljL2hoGj/FL09y49sDmd71ikryrjyiDGG2V0kuGlL0XhzkAv78SNVpiqDRI+OOOdxiJLaQY37d9ngXvdnuLvl1O7EmYQAQhogbqcDGbwSxsEb2Vkc/hb/B9x+A/DKzikpS/fSa2ci7C4CoitWLV4gh9exZXPUlEslxSGmQWgVwWrkPQculGX+j3PaeNMRaNc3hIJ3NdEzieTZ/gBDVNeXooZKWV4lEcvXITmTyUE5DPI+vmfHjRMf/lcFuSKz1kmmUKIzMe/TMO+ZnLFOI4myvkkgkMyAX8OdGps+SG9hNt3LmhP2tbX+wvLSkTZiRTFAUJiJeyRgLgv3B3mCXlnXBp2FRzdU81ths0AcQu/KBBS3bpdNG6k0gy3hmRxZHXVK++ByI830iiL4YxhpUUccdg4WH0rxmpelGTL51WM2/cNRjwI0/KoDScHQ6zc5mXvRGqvCU6Ith+iCqZAif/h8U0a9qjkF6kktKboSHNAG9RIium+1P0d9MOe0Sq7kS/f+0xHNqzRx7Yr6C99V+SFYKg+ATb2RRPBDLHX/94Y8NL/I9qtUw2eQ4prr8VVGpQtAy3P9Ocl4lufhMr3JSCU8VaWJ3TvBOeFBXGCxiavEAACAASURBVNG5wox+mtcq5poOhCH1PNe2DcOot/ygZHu2pIaJ+XXDsG3bdT1v2tU7C1p1m2/bzmd2q5jtbrfbNqONW0VrODqyVlOSs6BIZKX4XgoW7nRT3CUhPcDCHfFHrJhrOkJvI/K+5P7vxTnCx6KiEiSmTrx4Zyq0VYuAuq1SYQx+fn/MoCP40DBs75nV6Xa73Y7+mB7g5je1o4aAoGVsfgkQq9Ptdh2dukb5T5ZIAABf3/op/ydNQC8hWqN56xrd9NitZqSeHf2x1/RCVFaWlNMRz5k1E+J5qOeFJfZ1zHdFrl1HeAcncqeYby+g14s+MO4s9HpYeDv7diZVl/9UtxVE3iKPwrl9gkhePGLRyx2VSngGyBP4UyQnGw0gurW2alb2d/PUeOzU+fcP6sb9vMosrdHtLPvbWx5fc4fUc6nHC6GO84D8GB1E11fUZVWtVLThtjFp6pTznhZuYvTXqdIoRa0C/zp40Krf549EdOsdxKkkE78IcRaYlG6xoLWxRcPotd/Nb6eSSCQzM9oxjJ3U1/odne7stIL3+Bpea3Q7at2OOznneP6smG1nYLhRBQCxOk7VdkvLUuNXrlu7tue2lvMfJusoJWCDZ8A1PIq6UvU7Ot154LPbplIyxHdVb9yAmJfyXtJZC4EkEsnlIGg1D58T627VaxpG/N1FnRzQqEB8/uI5u2YCUU1nyfKZ7T/g7vCZ3n3abq1uNRTmPxyhVks+MGo19B767D1xu1zVRewvEhV6XdfndMYjkUhykAv4syaknkt/pZNwovEoaBluVIbFjVP+v98ly9jhIFs/pWhmQzMbAGPB/rbrUeDZMR+FDfpjHzRsv1/2AtEwtah/dUCH+Ov0UEVdAKX348CQkHqbmM7AhPn/YHvppJGQek1A9qGeGekyJ1kCeil5401gNDmpZOrDwiXq2N8uUSvJtC1XuwAAutM1hy3Djq+LfqLWcHQqLgo9e8vqdBvJHarYrxviQMhZSfw6hdVngZAwv+5SYnXWsWF71cQYk1tqpt4t8/s7XFtLhvjTvPrtzidzWLBLK80riDQBvZTwJPV187Zi3k47+TC/DlTYRGQPJvUqq2/8rifUzFxBjuLo+UkO744KWkZOGqey9A7xErckRK9VEbksWd9v31PSY0Ili1Q3euaO1bd3V6yq5/XVo361hUj9vNBIJTwbZAn9aROlm0eVV45OgAMaTmZz8hj4dN34Qf+3/D9xsxIAIGgZhlGPquYVpaKqBCDW/xrPO1MZzUdB96Lqexa0eJVWwauDlktR0RdxQCeDPYfcFf+6fpeXpt6t3QCAP3lZQVQj303FvNdqAERuqvLyNQC4UeMv7Tbv3LwGADWZw3zqFJU5lRdHSS4LilpNgnqjyR8XKgueXfcTJQg9e285+hMOPTtVW05dMcLLMI0tlYuARZKqUtETGsmghcwdQL3dFT7YNtNTX16L6nTze0kVs90tGCrbOywaEoWwUdVqsO3lyLREIrlKBHs034pNMdsNU0n5/xaK56S+nVwzcwWZ58jv7h/6W1QkYfLth8lYH8VsdzOLen5GFL/PzDvmNn2pFxY29zN/w4O1bs6+dpdIJNMhT+DPGEVbXXmdho9E7xQAvnJ2owb00Pt5YP5Q4xVQ4RPgiWcbHoiuE0of/1PdcENxnZvJFLkGbtcMfAUMPdv4hdX5iSio//2DunGf77WyB/8cAjd+P4p/JnXt0qL56CF50/6QHkTfEWXwutNtaGzwDLgOPKWbNt2MrriOL38d4HbOOrzXi5bvAG7/b86vDZf2Nm1jM7nkxv8gzU8kkrOC+RteSKyO+KMUpesp/7hkEqgt66CpWs54JFu3mTI1EnFG8aRvPCY532iT+fWco6MpmPCUGg5CAINh6ZASF8KKOlmShHccG2n6eBWRJqCXj7yEnYlrjhLPCX2bg2bmCrKivgL0vGaoO1y7+Pr9yF1IfkYkbjle7ikIU1l6Jb8IWB1TwQm9Q+Tfy8VGKuGZIBfwZwwLHvzzIwCL3zVTO7eZ/vCDdottNbRGx3r2oXfwBKjoltNYevBdirCX1zlPiP7OW/3N+wAAxfxfyP+5GT7HcDBkKPjU+er/+Uf3y8ljdEF4OMT4vJXlN+1neO3du3/z2SaN3J3vvtXfvJ/TsvUYAF6Pl++AmDe3NkRL/8J1jJ6Sb78z49RZFl9NiSxzkiRMpJ+PTxiL44CyI3lz3SQpI6131dIXBS3bC3WnPUMdjvCNjgLouKPSkUOi2JRYnW47yuc0POlEL5kWmZB8Fcg2gBOr0zaPFM8JfZuDZuYLsnbP0Q2XRvlGwV5mF6DwLfFQoROa5ifLd8nVRirh2SAX8KfNeHQTMg2k3Fs0leke+BuuR7eDRkNTzO99e/fAg+U0TAXB754gyVrj1yXdnMyvx7d/a0VBCIuHu5vtrgnm1+9Ho3/2KvDk6Zf/0en8RFN4YFvySPyTgtwq+zQBcrqqeM/+/QcrTqebvBEKTHjRscEzAPoH4yKvmI32klq3vRCjp8Q66UeJRCI5BmzQH/Mz4lTndP/UrFd3uo2KX7e9suupu2dZBLN6yAk3Jf52iNXpWBv2Lp8lFw4xf4umCoMiFynpYieRXF343CZ1+Kw1ut0GEJXN49TE87iaKeDmm3tBQ9PEKX7pBqSY9KVdTSr5HsJTlCHI5btEcnbIBfx5MBgyKApisxCxegegaLz4aiIRPtijvGmcXxldN32wSAbd4T9R4fVb8aRVWVohs92Tf2iM1/VPwvtKczqyAGA4CAkhYRiGnlvHrBmqskRnWmSZ01Un2KOR1ZFaBfISL+bzc1o8dzK++5EeHcRaNc3hIHVYflzieTZ/gBDVNaV0KBiE486ZFZVk5u4SSTnSBPTSUVEJNwYqdH9XTkM8j6+Z8eNEx/+VwW5IrPWS9bvwJc579Mw7ZoN+WQ0WICazYZjZxfBsY/e0PlMkLzZSCc8AuYA/bcYrMFnQsl3XhtNtaKL/cnLnFhj7uOB9WBk/pdkX25MWqbEwK2oV6M9yT8Vsd9S4l5/ozvrqYGNiNc8XDEXZJlqjqwHidzTzxF0yJbLM6erwxeeYrAYi+mIYrd8nm8MjD6V5/RGm5Yt35FTzLxz1GHDjjwqgNByduple0nx+/1vg8y+AjNImhadEXwzTB1H5QxWVAP/6L/9guLy7iFidtUEIsiLNOCTHm4lKCb1EiK6b7U/R30zNuIjVXIn+f1riObVmjj0xX8H7aj8kK+uFTxB7I4vigXiuyl9/+GPD64lLazVMCmFWdRWz3TUn8uVkDvzVJp5k/snhf5GqOHekC/1Zo2gNRwfo3glNPl5AFK0Ruc23G5oyZmoKZA78ym+0apGsIb9EIjkhC3fSgRh3SUgPsHCnEc2/1nQkDsjC/31O06+KSlL27rxxaCq0VYuAuq1StQx+fv/J2Hc+NAzbe8YdnDv6Y3qAm9/UjhhSzLcXMDx8dKvJrZ1f8+w5/gokF5WiqA7JVUFrNG9do5seE9LAlcNreqEwIz4N8ZxZMyGeh3peWBKiwXxX5Np1Ejd8IbSK+fYCer3oA+POQq+Hhbezb2dSdSPHvOh+6UQ8yRVEKudpI0/gT5FRjwHhxKmX9XYc2F5RCVAtdEka9RiA3gjmLbWKsS3ezIZscqUy2bNVZCp6Ah5j8sQLLPC3t7zoCF5/jUZn/VmjvtQ5ILE6bRN+3fbCxTvWKw+jF5PF6lfAS/N8YkkesszpKjHaMYyd1Nf6HZ3u7LSC97j+CAv2uB5ojvZtop08+ssnVsep2m5pWWr8ynVr1/bc1nL+w0wc+QDCaOMaHkW1Tfodne488NltUykb8h+OULn1+mFTuNDjOvBaed+nRCK57ASt5uFzYt2terE0AFjUyQGNTijmL56zayYwbm6fA9t/wAuNMr37tN1a3WpwKazVkg+MWg29hz57T9wuV3WnKrSXSCRzQy7gz5qQ8m1PtYKoVMmtq07U3C7c6Sbkn+uxt+GrsYld+YYs3RPeS9kM+XnA/I97k9/7wPZGydchpSGwME2IMu8FONhJyu3D8CAEFo7OPpHMTHrRLtftl5433gRGk5NKpj6kRT5xY402RK0k07bxpslkRHe65rBl2PF10U/UGo5OxUWhZ29ZnW4juUMV+3VDHAg5KwoOq29Et972wiL/JObXXUqszjo2bC9+hdixTL9b5vd3+P7nkUN/95OfNJPb215m13R6ZBbG5UBGdUj4ufK6eVsxbzdS32d+HaiwPIeMiS7FjL7xu55QM3MFOYqjb5tAVOw4YTkMAFCW3iGeV80oYd32oCL2Zvp++56SHhNSWKS6XHf1NRGP1+h2j/zNFiP186IjlfMMkAv48yEqR+LtVXTM+y3P5K3cJW6hpuAQtQV+6bIOSqlnU34pz5Cfz4PzzPpHE98f/mqUc/WI1wEIC/vUB4nIa+LeeYr6St6Pkgfwp8NYFRP/Uq7hrySZap1o8teNZoJ22kky9Ow9h3u/BS3DtVtqPPWjrhgJWobrGpRYnW5XZFokeW2G249z1Zlft9N3APV2o/Q2MH83fkBei1p0mhVlYzB/cqzEMnm2IYlEcgXhjX8558qK2Y7X8+XiOalvJ9fMXEHmR0K7+4fYpcTqaIi2HyZnlNlgIUTbnqoi7PIyJ+lpc6RC1WWDPgi2DEMsz3K98SQSybyQC/izhlTIb4fhy38a7dtqjW4TH9yjo+cAgGvXa+//6N7tHNVTzHbzjx/c2+EXXlvQ//ZlSkNRx54uoWf+FsUNUnkSDgGiW+uNmx+bFH/4AtDiC5m6veHSEK/zNT+CVt2lIfD6AvBy0bMLy1Ld0T93aWbBzgaPgYq++BI9EEXw+tp31a1m2k6fG/GpldhfNcprCvZ6QOXW4kuHyYsTY36JRHL6MH/DC0mcAiRK11P+cckkUFvWQVO1nPFItm6TTyYHQ0ARcUbxbG48Jjnf7Ij59ZyjoymY8JTibqGD4axDx9cimeNwSZBRHVecZN5Scs1R4jmhb3PQzFxBVtRXgJ7XDHWHL/z5+v3IYkZeqCluWdBzGR4hhcNBiFBf67a16Ja2MZixlUD+cV14pHKePnIBf4os1BQcjnW48xJ58u1YUJlfb6YWw8+f9jY3/Lf4xmXmXJ359eZOfOHzET9Tf+UbyY+KrgSAV1ecT9rxz6jFNan8wj//ZWQX/2gEYi0P6uJLPBrlbzZzVN3pNDQFQQt09OYbY6Mvqd9rd3+YvFV/K5NFEm3cBi07E7fMN7i/25xXv60svipB1jVJ8hkOwuxEb3zCWNzbmB3Jm+smpz1pP4xq6YuClu2FutOeQRaEb3RykpU0Ec02JLmqyKgOyRjZBnBiddrmkeI5oW9z0Mx8QdbuObrh0sgqONjL7AIUviXbC6E7Jzswz6RzRnpa0KAluexI5TwD5AL+tBmPbgKQ7NQKb1GiO+LEma/vc3KTWOsHXgjceNe5976mgH3a+sEmfQq8/I0ZnimkVHc6bU1hQct2qeeGhBdq8U+KQp8UzWzkfBeISnHjFn3ETfpk/Ermb+UkyRG1krLAk0fwEsnZwgb9MT8jTnVO90/NenWn26j49dxeoBjq7lkWQVGD/lFoja4Dw+Vvh1idjrVh7/JZ8mxDEonkKjJhChyvU1kkYqcknsfVTAHvttwLGpomTvFL90D5pC9T7l5RJ+ZtwIz9RUcd20skklmRC/jzYDBkUBTEZiHJYlXRePFVurgUADB89hQAntx37fvp77820wPoDv+JCq/fio/DZ8+WL2/RTwi2vTDbkcUGfQC7G3YY/+AwpK49c4iqrM8pQ9Y1XUnyc+Ctu9U411GtAjitpkXRNJPcvSwgctRjwHV91TSHg9SJeC6pw6lwzKmeaxsAhJ794xpQXVNONCS5msiojqtNRSWZUsJJlLR4piSJugYl1t3qTD/2WJqZfZzo+L8y2A2JtV6yfhddke++i4lTo3/9l38wXG5UT6zmSj995J9sLYyrbqY+oVab9pEllxGpnKeNzIE/bXSnm6Hj6CF1bZ63ORyEfOc2he2F/OMiDe8xv7UYb4wSot99N3ebdAomLFJjYeafRLOgmO2Oo0dPRHSn07Emn4+Xy0+2u4YhdKeT/IoIUhGokvlRpKFSW68A2Rz4jgVvM16/89jh3f2U7gQtw6j7U08bjyIdScwzMMt45S8U7sJcIgTx2VHHIsBCKsc4cczkke61Xg8Lf6GdZEhyteFpHbFO8kxjGWt8BRAh72XTkUg8mV83uJediEGPRPar2X708TQzeeKlFQK65+/vlgXBi7533XGq9++H2ddXr2N4+Jwnunedqtf0wnjaGIlkxyJArRarLvPrhpHWz8VeD6gtSwF9MTgvyRpTTskckQv4s0bRGo4O0L3jr05fqn2vHc2+2+3GW392Co93EhStET1fu6Ep3Akl06fF1+9ZRVfUKjJVCFC0xrpFJncxJBLJ3OAWbTFimroRrdi5/3txjvCxqKgkNQVmR2VgJmirFgF1W3lqGWxn+jbfvGuRSFc/rbsUC3f4yRDzt3q4jtFDn808JJEIxibBchl/+dEaHYtQN7OdGbQM2wvFEluI5w+iWvRhJJ6K2e7o14HfDo79Q2fWTIjnoZ5Xtn7nxnv6InUnvD7Yfv8pgEcDfgCvLdcAXK8uKRA9kEkN5Wv/k0VAt3wGKDdvXEOsn0DlPy0A+K2cxZ07UrIuK7KE/hQZ9RgQjpet6tbbz6IvKioBqoU+nYm3/C21iqxDstiQrY5fqUz2bBWZip6AxwCEAX5CqosdRNdfo9FZf9qEJVPIS6xO23zpd9fy+seAa/z9SOaLrGu6qox2DGMn9bXudNQtO24z1xrd5u/MZvyXSO5GwsTl5TdJdeT1qHln1GPAH74IWskskOCzONP4mtAdxWw7A8NNaeHrBI9EWeoXnwPPeg/q3n2ek3z3RuoZFfU1IKRu/Zvd9u3Mu2GD/sRuoCh1xR8AXEveru50VrZ4jvHSTENShiSSK4xitrtm0DLS05RMPobW6N79f43NL6OpTDKofPM10N/++QgQyXDPbt0QsyASBbazeP5TuVVJfmRWMyv64jAd/tH/OApsI/pi5mn50ToAhIUdSOJAnx5E3+DzstiTD/odPZmqkdcR9vdZ6XyMffnkOV5/OfmQ0e9aZFPqp0RyWsgF/FkTUi9EtLLlzUpuXXWE95twsQsnspN4a2bsEhd5xBVD98S0PBHzecH8j3uT3/vA9lLJciGlIbBQUr8Vs/BXr+Dwec7AK3+1kPNdyVyIl/EyB/4q8MabwCgnkS3Yy+TANw+VVCfnZl3kYSzUFByGm3trSeww/Rs1DsoId/b+Ps40pqH3YCXJNEZUd7mXbuhkft32InPkN94E6OFn30nnJIvQDAQt96AgBz6VY8z/G7SMdI7x32ZeFezFOcazDh0LmYVxKZEpHleYMZf1MYJff4lcqbrd7ma2Hg8OX7U6n8Sd7ft9IK26HrGE6xwPsMsOcf9jPnFEyno4So2LE+nv6t4mnI66NZZIH5GOgWd+3fZSB0lDAET9nxvd9xrJBWH0ujXdc73twGyITY0opo4BwP+41v3HlH62NqV+njNSsi4xsoT+fFh4W7jGmWs6EHpu1AZvu+MmbwJt1SLJlbabXb4v1BRAETFy2rIOgIrWetuDrs/aLT8BC1p123s08f3hr0Y5V494DZZitrvdbtfREXkCODoQRckp73+S1y5/Xb/3vty3PTVkVZUkg4gyjtIdFXPdIpnGz2zscLrBJZNpnHhcKEsrhJsQ87rLVD2+aNJMFcafNAd+Msd4knAwnHFIIpFISpgmLl6Q0roS1S0TZN49tJaaRPIq9vhV38UDSqxVbVLGp2HCD4WbNUUV9Y1ux+q7fMLq9q1Ol28PSP2USM4WeQJ/1pAK+e0wfPlPmahw1xrdJj64R0f8DPra9dr7P7p3O2flqpjt5h8/uLfDL7y2oP/ty5SGoo49XULP/C2KG6TyJBwCRLfWGzc/Nin+8AWgxRcydXvDpSFeF4fcQYtHwb++ALxc9OzCslR39M9dmlmwc5M9ffElesAr6Im+9l11q5m2008+4YS/6noynzfbHbW1sUVjJ/qK9aOGXL5LJGfFhc6Bn0+O8TyRmQ6XE5niIZmOnLh4Lk5prStRXRQPVca6h7RGtxvd8DAE4DXD6Eh+XManQZyyR9X3QStdwxm0DJcSS9RLMb9uG96Uu6zHQP41zQ0pWZcXuYA/RRZqCg7HOtx5iTz59lKyBVtvphbDz5/2NjdE4Sq/AcS5OvPrzZ34wucjnnX0yjeSHxVdCQCvrjiftOOfUVOimlR+4Z//csPlveqPRiDW8qAuvsSjkahCzUXVnU5DUxC0QEdvvjE2+pL6vXb3h8lb9bcy6SvRJD5o2dnlOx/VGm2tEX88OLPPxGXx1ZHIqirJGBc3B/54OcazDUkkAICvb/00t1JJtiBddaaIi5+kRHVnE2Q2iAyWspmh5a+aRGt0HRgu/+nE6nSsDXtXrcQedvHsTXTqb/mr2pLUzxcRKVmXGLmAP23Gs5cBRI1MiLxFie4IE3a+vp+I5ARY6wdeCNx417n3vqaAfdr6wSZ9Crz8jRmeKaRUdzptTWFBy3ap54Yk1U0VFkaeamZRBxj/9Ipb9BE36U9IOvO38pPkphiVSCTzJBUL8eLkwAMAiDVVDrwoCsp77oyMsUE/VSkw25BEIpEUcHRc/CRKmeoWDxWrZ+kNj0em4z9ohaiuKUAwCMePeCoqAR0MsQRI/ZRIzhC5gD8PBkMGRYFwAk1HqCmauW7t2pMVT8NnTwHgyX3Xvp/+/mszPYDu8J+ocG+8eENVWVohxwgrSaGtWoR6oeeWn6vx5q28Hv8pRqdE1gUdjayqumJ88TkmNxOJvhhmcuCzORdBy3D781vST2YaV/MvHPUYcOOPCqA0HJ26OfuZ4i6RP+e48zMvGz38seFFbpu1GkBWKlMM8TM05tftwZqDkA9JJBHTpHjIjI+rR+Tu9in6m6lJFLGaK8UvKlHdMkFWqwBNJokpE7rZZPz3v51MFUo1OxF9MYTuaIi2KfY+MtzIvl6/o3OVVCpSP19Mpgwekqp14ZAmdqcNN21L6Dh6SF2bBxtzbxAvcrATpnPhZAQ67zG/tRgfaBOi3313Vmu68aKmZI+Ub+DOgmK2O05slkd0p5PnTMdP/IoO2MtHJXOjSKClcF9qFu6kleguCelBEtt74XLgmc+dPInVEb6Y1DXEhYr59gJ6vegN31no9SLj0NKhNR3UbQW8pf7xP83xNyC5XHx966f839j3pTno1UVrNG9do5seu9WMp3v6Y6/phagU5PGUqG6ZIHNZFL51CLa9MDr2mEXGg5/ffzL2nQ8Nw/aecWXt6I/pAW5+M7p/DSE9uHlXfIrcpDsUteSJpX6+qBRJFqRqXVjkCfxZo2gNR6cuPZ6rCADgpdr32s10j3l9zs92QhSt0U7FrAR7E8VWqYrdHMpHJRLJiZjIgb+j052dVvAeX8NrjW5Hrdtx1+UcnYnGMo2J1XGqtjtNvanCS5Lc1vLYw7D9B9zdONMnStut1a2GwvyHI9RqyRuu1dB76LP3zLIhQGt073xuuNwGotebvzeTRCK5pASt5uFzYt2tek3DiL+7qJMDWlxHXqK6JUOK2e4gHsq0EB1PxrNWewI2eAZcw6NIWfU7Ot154LPbpsLHbtz4ctMwNgEAN27gyTOh5FI/JZIzRC7gT5FRjwHhRNmq9XbkM8LrkaqFKpd4y99SqxirjMpUoaZd6CfcVPhBf3WO7wyPMVlyBRb421sed8Mjuv4ajc76087TmULe1AcP8z9sUzGaeAJITospq6okl4OCHHimPqRFPnFjdTpErSSTvfGm82REd7rmsGXY8XXJbNTRqbgo9Owtq9NN5UxUsV83xIG6sxJ7bkK01eQ4ISlL7xAvHV4c5c4jbk36fvuekh7b3WfmUvEQNwHZGRGrs44N26s6J5h9SivNK4g0B73KBHvc3u22Yt5OmwUxvw5UWDwfS2vdRGV7ieXbbG5wZa9ift2lsdwlpsTDQZhd+DO/v5Oefr767TjIPmPRJ/XzAiJV6+IiS+jPmpB6OweRrPI4ZLfuB1HFPAv8umEYEyWj2rIOhN6GuDLyiCuG7okiqrhRdG4w/+Pe5Pc+MGxXrN4BhJQeAAsFhWOTr67bXu9p9GVIXbvuH+lzJTkOvCxqrDKqpKpKcgVQ1GoS1MunYrxsstux4GX+CEPP3lvmZZOOHnp2SqKoK0Z4GbuxpfLSS4sktZyiEzOqLLWQuQOot7vCB9tmesLJK0Cdbk4Hp2K2u5kNCb5PGR91ZQ69sr1BhUOK2e7m/SyJRCIpI9ij+Y5titlumEry/bTWKWWqO+XQmJiWy/j4kxXKXdHCX9ToR+1Q3LYomudJ/ZRIzhB5An8+iKZLYXxCx7zf8mzcyl3iMjFy3JmOejbllxJdJ3ROa3gWtDZc+mji+8NfjXKuHvE6ABEgF7QMl/J9XREWJ7zz+CHbgjgjrAT+huuFmXIDyQkYW7TzL+WiXZKB+RtemARkiNL1lH9cokrasg6aqn+PR7jyRG2PKVM45m9R6Ek25GQ4cV6vJPPrkWBM8fh1l0ZPUlByFA6GUIuHME+1kWaQVxFpDnplYYP+tIfkKa0rUd0yQQ62vTDWUz6JFHVUR8r4NEw44Q0HIQChkaJGX3QJiACjeSP/Xs4OqVoXFnkCf9aQCrkBvPyn0Z6o1ug29YVr0fC167W7+Yahitlu3okvvLag6wS8jh2ihJ71RoCIYrtB+CcJ0a1O+1u/Y9GV0YUsaNUNwzA+/pzfT3wZf51L0DJsl0J39IWxEW6yp8cme4ToTtMioHtB6pq++IQToVKZJPjKrZfEqKKZa3pyLiiRSE6f4SDMGMWL8qDkD7i4jTM7kjeJTU7LmV+PrTrLXxS0bC/UpyvD5K5JuiMPfyTnfNkpyAAAIABJREFUiDQHlcQErbQ3ceoUPK11JapbMsQG/YxbkNaIz72PlPFpGHPCC1rpGs6gZRh2VEPQ7azs2jkVo5KLg1Sti4s8gT9FFmoKDsc63HnQO/n2UrIFW2/S5PT6+dPe5ob/FlfjzLk68+vNnfjC5yN+pv7KN5IfFV0JAK+uOJ+0459Ri5tK+YV//ssNl1e7PxqBWMuDuvgSj0bjxnNpVN3pNDQFQQt09OYbY6Mvqd9rd9Mme1uZWFBxDo+gZY8t3ysqAT38j/zeURj8bB1fkN1TWWSDk2Qa2KA/5gfHqc7p/imzJN3pNirpzsk8qLtnWQRFDfopuMNGpp20ouYGdBC1UjYkkUgkszFhPRTnqLNirStR3dkEeV4yrjW6DgyX34dYnY61Ye+qFTE5S83ehD3p0TItkUjmjVzAnzbj2csAkgInEaiUmLbx9X1OxRNr/cALgRvvOvfe1xSwT1s/2KRPgZe/McMzhZTqTqetKSxo2S713FDUQfG5cKE5tGY2cr4LRJ9e3oavrpvxG/FCYGK2zFfn2YJZxVx3Bhuua8e/KSIP0ySSUycV/KBWAcwv9X3s5/Cam+TuRxpcEGvVNIcDz/U2/KXih+L7AjlmUJm9QzGxjSsFSoYkkpMgzUGvKjwhfYpgjRRKmeoWDxWrZ+kNj0e8AQEAQStEdU0BgsFEtlBFJVnTZMlFQ6rWBUUu4M+DwZBBURC7JSeW64rGe5YmQuaGz54CwJP7rn0//f3XZnoA3eE/UeFNq/GGqrK0QqZNaM5S3qKfwD1Pxnv82XCvn/mxxZsIRyP7djLIBicJ8MXnmNxMJPpiGJdiTvQ9Rr5z81rSpws7MxEa44x6DLjxRwVQGo5O3cIOztifUxw5xe3yvMX+8MeGF7lt1moAWamUDkX3jMr7w8nsOolkGorClktGJRcc3orubX+K/mZqEkWs5krxi0pUt0yQ1SqQSiJmft0WgRyzyfjvfzuZKpRSQqIvhtAdDdE2xd5HhnsgrtPv6GEioKlKq8d7M8/hJOfBSXRJitu5IHvgTxvd6WboOHpIXWEaOhyEfPaZwvZCvpGbhveY34p7zEGIfvfd3GLQKRivF02On7JmzcdBMdsdR4974HWn07Emn4+f+I35VTF/w6UhsZxO/CsiWZtryczIBidJxMKdtBLdJSE9wMIdsUQd63sU/u851nKzUFFJyrmYHRWhkaCtWgTUzdMC5rtRFlPigC8uVMy3F9DrRW/4zkKvFxmHlgwl1fgdiwALqRtKJLMzFv8xmQYiuQxojeata3TTY7ea8XRPf+w1vRCVgjyeEtUtE2Qui1tRj/q2F0aHIrPIePDz+0/GvvOhYdjeM+Fyrz+mB7j5zej+NYT04OZd8Slyk+5Q1PgPiGyKux2LAK9QW8rn5UeK2zkiT+DPGkXkIU+csR/NS7XvtZvpHvP6nJ/thChao50UXSHYmyi2SlXsJkRlCGZShtBYm/FXJJFIihjtGMZO6mv9jk53dlrBe3wNL9yF4/7JKf3fp0F0SkYVAMTqOFXbnabMRtgoTx6Gs/0H3OUy0/FJ263VrYbC/Icj1GrJG67V0Hvos/fMsiHh7dw2FeYDePOu9fLmZDGURCKRTBK0mofPiXW36jWFRTsALOrkgBa36JSobsmQYrY7iIcyLUTHk/HUiXkCGzwDruFRpKz6HZ3uPPDZbVPhYzdufLlpGJsAgBs38OTZgEEDjxppaKLG/691MpKTOInk9JAL+FNk1GNAOFG2ar39LPqiohKgWiiwox4D0BvBvKVWMVYZlalCTa5UJt1UilKVTsBjTJZcgQX+9paIgie6/lrsRJcqxkKmkJdYnbYJAPhq8KBVvx+91vruDP1kkgJkg5PkjTdFSOPY5pn6kBb5xI3V6RC1kkz2xpvOkxHd6ZrDlmHH1yWzUUen4qLQs7esTreR3KGK/bohDtSdldhzE1HG5KTJnLL0DvG8tHhysygV8Z7g99v3lPTY7j4zl0qGEm9nbrjJ/PrMKiStNCUcaSN6RQj2uL3bbcW8nTYLYn4dqLB4PpbWuonK9hI3zdmMNstexfy6S4nVWceG7VUTU+LhIMwu/Jnf30lPP1/9ducT8d/Eoi9yKY7+y/z6AWZF6ueFQIrb/8/e+4TGkeX5vt8esKFtMVWixs5FZNyXYeiaR90Eg6hRIjfiEW9TjKHj1jRpLwSVRRVjRnF7kZOL8qJjFrmY8EI9EJOLnlRTlxkqBvRACni6sXDj1aERVyZEIaYhb9FdAxWCzFiki8HVhd0FnkXdxTnxNyNCqXRKtuzfB0NX5omIjBStr86J8/t9vy8WKqE/a3xm7xyEssojPsy244UV84HntLWcWI7GDRXw7Q1xZOgRVwzbF0VUUaPo3AicTweT793RdFOs3gH4jB0AlYLCsRSSXANGbDdxrt21M2EoxInhtUzJdTv/92LviniZkORaHNjIp2K8bNLtt2Driewj+La+f4OXTRpqusOFmWKEl7FrWzIvvWwpySgizTwS1xZXT4ocs8Ngol4zOeHkFaCGm9PBGUfTCfhzymirK7Xple4Nyh867oIEQRAFePssXy2kZq/TlOL3k1onlanulEMZMS2X8eyduXnSChQv/EWNftgOxU2NcqZqIpD+Nm2/E8RpQTvwL4aw6ZIbn7CM91ue7JW7xKVi5LgzHbN1xg9VVFVhc1rDB561YbJHE++PfjfOOXrM6wDEo9mwQ6rT4P+ZSYLPcplmzjOS6UHiL2npTpQhJlxhQIYoXU/4x8Wq1LihgiV2pqMRrjxhxyX3ixuOACng1ZXRrzsfSpZX5rVpBk47FIwpbr9tsvBOCkqO/OEIcvFQ2kU5dcGTQw6RhIBsRF8HguHRtJvkCa0rUd0yQRbNPslJpKijOlbGp2HCCW809AEIjRQ1+qJLQAQYJQnrsRJxSyeGfjXOByRuLxTagT9rlKqyCFz6YfhMtNFxu2rlYjh8caG+nm8YKjV73VvRgRcrqqqA17FDlNAHgzEggtoWFf6XRFFb/d7/84cgPDI8MPCstqZp2qdf8OuJl9HrXDxL000G1VArmRFusqdGJnuKohrdlgK27yWOORJ/4USolFi+B8MjoKq+r8YOfer77ysY7JMBCkGcEaNhZieFlwfFv4TFW9HpkbxJbLy5HTjtyKqz/CTP0m2ft1Qei2fpYfv6FEe/iAsSrytkI/p64llJb+LELnhS60pUt2QoiJt9APDEN6FUx8r4NGSc8DwrWcPpWZqmhzUEbn91T89WjDY6vB6rZuta4e4/8SpA4vZioR34U6RSl3CY6XDnQe/KT1biR7DtLot3r589GWxuONe5Gqf21QOn3d2JDnw25nvql9+KPyo8EgDeXDU+60WfUY+aSvmBb/xmw+QV64/GUFo3hm3xEo/GWeO5JLJq9DsNCZ4FNn7n7czoBfmjnps02dtKdZCGLVKepSeW79G573U6H3fic9u71H06E9SVRMxAMDzK+MFxanO6fsIsSTXcTjXqnCyAmfutloKiBv0EkXV8vNiuyrkBHYpcLRsquSBBEEQJE9ZDUY56UKx1Jao7myDPS8YbHdeAZvLrKK1+v7Wh78lVsT2UmL0Je9I8meZFoynjJoIg5ggt4E+bbPYyUpVFPFBJUQ0RBc/X9zkVT4H1ie0Di+8b9z5uSAjuW59ssifApbdmuCefMdXo9xpS4Fm6yWzTF3VQfOpauHBuNDs57wLhXy97w5GFmXzYpD8xWw6crUyS3PTnEgQxE/k58K31WhQLIdcAnNaiVdTcxFcv25YZDwJgQb3dbI6GtmlvOCvFN3W/rW36AJ+0DlMPS1NhxcuqkqgUKBn61zsaf0zql38wQUwN2Yi+BlSF8W4w3NCnzYGXylS3eKhYPUsvWExeDnxVVgCurXsPf1vzUVuTAG/oA+p321r0p0Rdbylp02QA/Int/1bympOIVwoStxcILeBfBMNRAElCnKDWiRPUeM/SRPjG6OkTAHi8a+q7yfevzHQDqsE/UeJNq9EDVWllVZk2oTlNeYt+DPc8STeWTnvulLzuvTfUlUTkU7nlfvpB9Cpw2vpmHOs40fcY+s7NaxGbLOxMRWjkcvnPJEDqGCozCzs4D7vNTR8Lan+rI8GzNNPUYLidBqSV1Ypts4Owf55v/1dWqygbAu7/rbYzRt1w7zV4w1DmmQBBzM7k1JZmva8QvBXd1vVkW3jgtPWufbFwCVuiumWCLNeAREJb4LR1Ecgxi4zzHPhUW6QwKeLfg2tk3WiIGwbbYUn9tAFltZq8C1F1v/jNrN75xMtORrtIwV4I1AN/2qiGm6JvqD4zhWnoaOjzvaMEuu3zyvMkvMd8aTluE1fU9fdn3aLOamrcw5o2az4JUrPXN6I2dkU1+v3W5P3xIPisX9V05xJTQV1JxFRwY6KITN+j8H/PsZabhaqsJJyLg+MiNGIat1sKmDmRygEgsP7h8BmurW/xNLpGp5803fh+8oTvUT502P3nr7Cg9u81Eh9NRhzEKZDMB5l8Sbwq8AnVMyzU8iN1SlS3TJC5Nm2FPerbsd/7SWXcs7S4GCkksHoMUN5LmBQB3wwDANJKbQHA4htiCll9YxHgXy/utg+b5h8/rpYbFRPnENKulwfagT9rJJGHPLHHfjwX6h/1uske8/ac7+05kRqdnhsX2Xv7frafnq/fb+R87+PPJQjiuRjvaNpO4rVq9OUtPWozb3Tc7h+a3ah/UlkPd5/HgwDAf8R97Ath7c94EAB//NKzzMjnSMFvoxz4i2KbXXRKJkr4ryp4JHp1vvwCeDp40LZ3eTby+mLiHiX5CuAzs/3nbu9m8ssED/7tCYCvNjVtM/n2rw87jcrDvUdQ1OW4Z2BZVQ6YyIEvGFrB42fAM6an+gz8/3FsEz5BEATPge/35G0t2YOurreOfo/b/EVW6/q9ZqHqlggypGavO4iGLi51nUijSs7KEubAfzhodw8T74/wBFjwI2lV11vYDLvZ5SvABezqmigFXVwE3pTFTbmypQl7egCL7xsknQRxatAC/hQZDwLAz/adqq13n4YvqrIC1AqrNPm8eTBGc0muIVMZlapCjY+UJt1UilKVnoOvMdkzhcBztrdEFLyiqldY1hvq5z0G3okbd/3HJ1uhsZ6i1C/7VHs1O9SVRGR4+x1gnJPI5u3HWhE47e6hJIotA6etb7aFnWalLuHQ39xf47ZMnqWZ7C/kyGfT39n/a9ftiAHffrDad90w4Vj8FoezW7Gp47R1O4xBevsdgB3+9qd9tycG90LPTXiWeYDcJDnp48/cj9PfxdJM9uO/XBI9orUbf9frJAfZUfgidyg02RSEccozdhC85laaRAnkM/oqwncnZCl2r4uJHj1mta5MdacdCpy23rW8UCNLzsoSSl6AyOU4QvlpQvoSm0Ui+TjVgpQwZW7cbilsb7Xfk7c18+hPZ/5pkn6+nJB2vVRQCf1Z4zN75yBcnfKiI7PteGHFfOA5bS0bywGesAzf3hBHhj5vxbB9UUTF84znSeB8Oph8746mm2L1DsBn7ACoRG2vgdPW7cETRMOmHgWMBE5b03QzPtcfDJLnEnnwyqWS4qXvl37F/53lXRHnFREgHFY8Ss27LSWuekc2Bz7Z5ZPKgY9bZKSVVYV7GHHrykQh52S20fxz4Cfxh6OyoQjP4q1M2agMgiCIXJ4rBz5PdcsEmefAhzrbXFOjevpjZXwaqrICf+9h3MbJez2FRjY6br91ZPKOT/Oo1Xd7qe8Dkk2COBNoB/7FUHk31l7bZBn/tozJG4DjnN5SMXLcmY7ZOuOHKqqqsDmt4flG+aOJ90e/G+ccPQ7rALxt2wcqYhew6jkbpu2HBQXStcWL8J9lzr0wnxt+Bcks2vlLWqgTz8Vo6ENpZQKE7YSd5vPmwPPdbR50Ic4rPUnkwPfOPgdebKF5lqZrezPuwZNhJFEI+Yy+BiSCM4FkBMdEDny+6qJ4qJqTA3/8Bafv2BR+fGEMx0QOPPe364lSAF2zxVNWvnzvz0OE6RfhJYW062WCFvBnjVJVvhn5l34YiAr3Rsft4s49NuYL2IsL9Y9/ce9mjgJKzV73uzv3dviBFyvqjy8x5os69mQJfeBsMSwq1cf+CFDU1t3OtU+bDH/8EmhEBwby9obJfFwV3qOexaPgr1aAS0X3zv8kKaqhfmGy1IKdm+ypyxfYgaiCV9c+lLe6yT8c1aULo0NFrgJSgz+3CEt3v3r8DFeV6iN/JM597/rR5i4FiBLEWUE58BPvUY4xcSp8v/Sr3Mopegh7jqEc+JWHc1u+Ey8rpF0vFbSAP0UqdQmHmQ53HvSe6BkKnHY3sRh+9mSwuRG2LKX21QOn3d2JDnw25nvql9+KPyo8EgDeXDU+60WfUY9anPiBb/wm7Dd/NIbSujFshwXsj8Zl5nGyavQ7DQmeBTZ+5+3M6AX5o56bNNnb4oW2DYmHmxz+iJ8dhsGHk+bR0AfUtX/6H/HPybM2dyejRafj1e6eoh4kYn4kXCVfnhx4AIDSmioHXjxTzLtvIT3i84ZHyRz44qEkk24iBEEQeYQ58GhMrxYvSw58HqlWfs9K5cCn1LIqK2DDUYA9H76fenZgz17CRBDEcdAC/rRhGRM7AEr8lJIHKsWWbnx9nxN8HFif2D6w+L5x7+OGhOC+9ckmewJcemuGe/IZU41+ryEFnqWbzDZ9kfjJN7MK/wg1mp2cd4FwqmtvOPLdZvRFbB8Q211S864x3DBNPfpZKFG9a17z2Cx/DAmCKODLLzCpRYq67L+EOfDjQQAsfnd8Dnxk7yG2nKJ2eV42evhLzQ7NOup1nlVcNpTMMebf/t8WyUuTOA3IZ/SVQ9Sdb9/H0WbCn0hpdVeLT3opcuCBb7+ZNCVONDsp6rIPNc6BP9r/+zh7Tr2l+lBWq1Kz5zbTfQPI9zYhzjWkXS8PtIB/EQxHASQJYh6rtGJDdqnRvNva0ydblkZPnwDA411T302+f2WmG1AN/okSb5ePKqKklVVl2oTmNOUt+kAw2j9KXbj4McHz8oq34lAPEjEjlVvupx9Er+63tc0DVG6JtXGm71EECM8pBqgqK2DRQvyEOfDMNq0bk052gWPaURaTBM/STFMDX39LzXcr9s4g/ML/ekfbGVduNSWUDiW6RUXb5+Nq6x5NQIlTIpr70mz4VaDR6S79r+6mfXGp63aXAACBtaZ3baDaKs6BL1LdkiEui2F7Ec+Bv9s47oJFeP+y+xioJN/5uWYOcFVs61hrOsO19fD6ddtkB9fW3X+8CeD+32qbDHXxAYmsEfHcYBoDUuKckJQpUqqXAVrAnzZZE+XAs3TT1PlEk1siT3YsAZnVLe8xX1q+cHggnomKRvGZ7im7qRTXj0pyDTia5ZpSs9eX4yQ41bh7e7gRruYDZ8NkvtIyov15a8O0dUueymCaSEA9SMSsTOTA31LZzo7lfcB/Cxsdty+39UiNpvR/n4ZMDrzS6hs13ZzmGZ7En2hOLuGDhw+4KXKq45P1rNtbHSlwPh+jXo+/cL2OwedO8EGzbIhyjImzhhxJXx08q3v4TGmt1+xurCFYVpUDVuwAWqK6JUNSs9dHNJRqITqZjE9smQNAMHwKXMSjUFnVWyrbeeAEN5sSH1tc/CoKiV9cxOOnXMmLu5GI8w3J1MsJLeDPGqnRMVRmspPZggIALtQ/6nWTPebtkoNfAFKj00sEoHr7YbdUWGjQjAsNOmvRDyHvocFostOKIIhZKciBD+TPWZFPXOYxnyJX48letuk8HlENtzmyND06Lp6NGioTB/m2vtXqu534CjU8bGtiQ91YTaYS8wCLyUJ2aeU9xbaTFiMidx6R4Pysd09Kju09DJorxUN8b4tPf/dWWzXbfo4cY4IgXiv49vPd5k2peTPZaxg4baAaRE4aSa2bqGwv6diZrZmn7KzAaZtMafXvYkO3EznwfPqVktajnWRh/ps/6X8m/jNp0Tca+lDXRKRo7IxPEMSpQAv4U2Q8CAB/ou+09e7T8EVVVoBa4RPS2Ft+Sa4h09qUaiNNutBPWi+NhsUNp7PxNSZ7phB4zvaWHW7Bq1dY+q/Htw+s9i4Lm6rWr0dd7qm2Lc/STKy3jqj5tBDqQSLmREor+FSMl00icNq63kY8wfRtfd/gtkaepZnJ8hlmihFexs6UVt91haNGHEXEI4OF74XT1lMFOMzeC4OJEDh70Q2KCtA8hYyj6QTxU78AyOwEpZ8SlgxFUUjydqlL/nG82laaxFwgR9JXCG4ImrPpIDV7Ke+gpNaVqu6UQxkxLZfx7J25TQCBMzlWNP0KW/1FO5S3bUe5dcHwCAq2NE38XzfXW3RKSD9fHkimXlr+5EXfwGuHz+ydg1AepZVVBcxsO17oKxp4TlvTNMvLnNa4oQK+vSGODD3iimH7Dr9m5PQ0NwLn08Hke3c03RSrdwA+YwdAhftWSXIN8Hd3o1H4bHPXRzU5bG843iG/1y/+f9tPeV4Rk/A2pOTq/QeHf8P/vcC7Is4rgbNh+3E+kNS821J8ezsWIqV1O9xMv6Hyh2/ZkcYNFbFvkbSyqsAfjiBiJxJ+Rlz49hMql2d2FDhtk01ZyM9lTtwJb02awB+OyobCnwLikCSCIIgpyPPiLSChdSWqWybI3rbtxzrbXFPBtvh871gZn4aqrMDfexib3Y+GfqyRjY7bbx2ZmqZpGn8qK5bpo6EPv7bmCvqre/rkTJYgiHlBO/Avhsq7sfbaJst4v8VT5Zhyl7hUjBx3pmO2zvihiqoqbE5r+MCzNkz2aOL90e/GOUePxd5e40YdbGLRv/AjsUQX1bW22RWnjcm99ERQhxLxvIyG0U4KR0p4ugFlvY3pkbxJbLxbnvA2ThYF5ZzkWbrtq0ZvmkYjz9JtH1GwxayEu+9zUB5ylCSOhxxJX13S3eWJ3eik1pWoLoqHqsOjKD0ESJWrHyvj05BxwhOOnsnvpYQ1BIHT1jWbP2VNBc+FVylo0DoO+v//SwTJ1MsKLeDPGqWqfDPyL/0wbIlqdNwu7txj42cAgIsL9Y9/ce9mXihxs9f97s69HX7gxYr640uM+aKOPVlCHzhbDItK9bE/AhS1dbdz7dMmwx+/BBrRgYG8vWEyH1eF96hn8Sj4qxXgUtG9i9Bl1VC/MFlqwc5N9tTlC4yb7CmKuvahvNWN/nBcAa4uLV+OPPjq1/zBVyhIWF6oG78gdzuCODuC4VHGD45Tm9P1E9NZ1XA71UTnZC7M3G+1FBQ16CfgjwRS5ZpVWck7UpGrZUNzXL4TxBSQI+mrw0TnYrScDYq1rkR1ZxPkecl4o+Ma0Ex+HaXV77c29D25Kmqp4h3+0J60WKb9YcEsjzgvkEy9tNAC/hSp1CUcZjrcedC78pOVuIaq3U0shp89GWxuONf5XDS1rx447e5OdOCzMd9Tv/xW/FHhkQDw5qrxWS/6jHrkCsUPfOM3oV38ozGU1o1hW7zEo3GZeZysGv1OQ4JngY3feTszekH+qOcmTfa2oiz3qqyAHV42+m4jfGzrp55Ex1fWTPYX/+158uVet+4p6lAiZoX3bd5oAJBrAOaX+p75HMtkqZbIoPx4QGndbjZHw2QiUv6FtcyVI1Ie92l/5Nyh4OGeD99PzX1tXds7rZ8JQRCvDjwh/WThuFKZ6hYPFatn6QVPRmo/3bN81NYkwJu0F67KStpziSCIM4EW8KcNy5jYAVDiXR6eiKyohoiC5+v7yCEkJrA+sX1g8X3j3scNCcF965NN9gS49NYM9+Qzphr9XkMKPEs3mW36SmyHYhcntDeanZx3gfDxs73hyFFQHG/SF9tdUvOuMdwwTT36WSipetfiKxME8dx8+QUmtUhRl/2oFDNlJQkg8p2b1/I1aWqRcuDMMh4EwOJ3EiB1DJWZOXIorhLae4gtp6hdnpeNHv5Ss8O+nXodUFarZUNSo+c2J1KVqJWHOE0yu1u0qXVuCd3d7uNoM+FPpLS6q8UnlahumSDLNSARZBQ4bV0Ecswm499+M2lKnGh2UtRlH6rRQPiYYv/vNfNAHKfeUn0oq1VxF7dUthMq6LVKqtKfeFUgmXpJoAX8i2A4CiBJiPPVOnG+Go89nmhZGj19AgCPd009lf1+ZaYbUA3+iRJvl48qoqSVVaXcG6+I8hZ9IBjtH6UuXPyY4Hl57dpyqEOJmIrKLffTD6JX99va5gEqt8TaONP3GPq/z2f5WpUVsGghHhznwBnDdWUyBR5A4JjJLCbugA++hpea71bsnUH4hf/1jrYzrtxqSigdCpOgousdxSl4BDF3JgtTf3D4NzQ5Pq80Ot2l/9XdtC8udd3uEgAgsNb0rg1UW/mWvGWqWzLEZTGsW/e2bV9p3W0cd8EivH/ZfQxUku/8XDMHuCq2daw1neHaenj9um2yg2vr7j/eBHD/b7VNhrrRlICV1Ypt/xp915UABP/8ob77+NpfkYCea8gU+WWGFvCnTdZEOfAs3TR1PtHklsiTHUtAZnXLe8yXli9ETeSK+t71o83d7HlTkXWMik2o8jLZp0Nq9vqyFVbmK6px9/ZwI1zNB86GyXylZUT789aGaadypIhZoQ4lYjrGO5q2k3it3lLZzo7lfcB/C0UEeqRGU/q/T4PolAwrAJRW36jp5jTP8CT+RHNyCR88fMBNkVMdn6xn3d7qSIHz+Rj1evyF63UMPneCD5plQ5lCe4IgiKnxrO7hM6W1XrO7mha9u6wqB6xYVEpUt2RIavb6iIZSLUQnk/GJkiMACIZPgYt4FCqrektlOw+c4GZT4mOLi19tatomAGBxEY+fDgM0pNFwjKsK08M6r2VVOWC/93CTJnkEcRrQAv6skYTl+slsQQEAF+of9brJHvP2nO/tOZEanV7ChdTbj2KZRaFBMy406KzN+EMgCOLEvP0OMJ6cygXy56zIJy7zmE+Rq/FkL9t0Ho+ohtscWZoeHRfPRg3HQPERAAAgAElEQVSViYN8W99q9d1OfIUaHrY1saFurEaWHRCJSTku9dLKe4ptJy1GuFmUjEhwfta7JyXH9h4GzZXioaY0GvpQ10QkXuzsPAuvmxMHcVLIu+QVg5fv3G3elJo3kx2BgdMGqkHUI57UuonK9pIguuky6k5yVuC0Taa0+nexodu12NNoNPTTC//AOdpJFua/+ZP+Z+I/Y4s+8f3T32jW1kjSz5cB0qiXHFrAnyLjQQD4E32nrXefhi+qsgLUCp+Qxt7yS3INmdamVBtp0oV+wg6V63Ftjt8MX2OyZwqB52xv2eEWvHqFpf96fPvAaodR8Iq6fj1l+RJ41sYW8/noF/unVF3/qsI326N9eNp7J6YjpRV8KsbLJhE4bV1vI55g+ra+b3BbI8/SzGT5DDPFCC9jZ0qr77rCUSOOIuKRwfxygdPWUwU4zN4Lg4kQOHvRDYoK0DyFjKPpBKPIXykAMjvp6dKigqFgeAQFW5ompia53ngEQRATcEPQnJ12qdlLLWOTWlequlMOZcS0XMazd+Y2AQTO5FjRwj9s9RftUN62LXLrgodHQE2WMnkjtEFDEKcFLeDPGp/xZ69yFaGjktmWw9py4WLnTygfb1aPXOJCj7hi2L7YV4ucnuZG4Hw6kekeOHd0O5Es5zPmAxXuW8WXCbu7fmJ4048bw5LZ0AAwZnrYzEpMz+S6nZb0xLQEzobtx/6aonQ94R+ntG6Hm+k3VLBE/Xs0wlUq9H3j6jYcAVLgbKU6MSfDifPM4gKnbbIpJ4Fc5sSdFDyx9IcjyMVDwNCHr665vXgmrA1n1CGynyCOgbxLXiWC4dG0m+QJrStR3TJB9rZtP9LTVN76sTI+DRNOeLzXUzzoFTX6oktACZ/KBgDATO2oFTbBO21ds0g/zzOkUS83f/Kib+A1pfJurL2Ab5u6xtFN209MlWMat1tKfKRuppfvlboESCJGrnFDBcBsfk3dhqrmZh/PQuBZbd1+NPH+6HfjnKPHw5G4o3rO6MKP+PpdPMQ1+q7rGiqAylWA7XvzuufXkR8c/k2yMT7zkiCyjIZ+yige0sqqkvw1LG7jTI/kTWKlZs91hYFdm6tS+vFjzkmepdu+akw1/fMs3faRCraYgUbHdRPTTam5poJtOcdG3hHEySl6qEoPW18NPEtL0I5lJKl1JapbMhQMj1L27o2O6wrpO1bGp0Fqrqnw7Q1xz56V3ALyLE3T91b7ruu6rttf3dM1zYoun4qIJ/0855BGveTQDvxZo1SVb0b+pR+GLVGNjtvFnXts/AwAcHGh/vEv7t3MmYZKzV73uzv3dviBFyvqjy8x5os69mQJfeBsMSwq1cf+CFDU1t3OtU+bDH/8EmhEBwby9obJfFwV3qOexaPgr1aAS0X3LkKXVUP9wmSpBTs32VOXLzBusqco6tqH8lY32mS7AlxdWr4cefDVr/mDrxA90XVTNWb/139Vxo9OzaWeIIgswfAo4wfHqc3p+pnCymrUOVkAM/dbLQVFDfoJeP1Oqty9Kuc+sVTkatlQPj4lHBMEcQwTnYvRtCYo1roS1Z1NkOcl442Oa0Az+XWUVr/f2tD35Cr47DK1Su8ZQ83ccm7fBdJZoRzST4I4LWgBf4pU6hIOMx3uvERe+clKXEPV7iYWw8+eDDY3nOt8LlqpSziE2FcPnHZ3Jzrw2ZgxALj8VvxR4ZEA8Oaq8Vkv+ox65ArFD3zjN6Fd/KMxlNaNYVu8xKNxfh8XR1aNfqchwbPAxu+8nRm9IH/Uc5Mme1tRl3tVVsAOLxt9txEWV/m5k+ZGx+3fsHSTt1XNBNmfkPUIMR28b/NGA4BcA3BaTd+eZbJUS/mxuzJK63azORomE5HyL6xlrhyRegKY9pcvGSKIM4O8S14heEL6ybYdpDLVLR4qVs/SC56M1L6KZ/morUmAFxmNRFRlBWw4km7UZkwwIl5iSKNeZmgBf9qwjIkdgLhDSSQiK6ohouD5+j6nZSmwPrF9YPF9497HDQnBfeuTTfYEuPTWDPfkM6Ya/V5DCjxLN5lt+kpsh2IXJ7Q3mkWeovzxc9Sij6hJX2x3Sc27xnDDNPXoZ6Hk1LtGvfBK6zmLYQmCSPDlF5jUIkVd9qNSzIm+x9B3bl6/icnNmZQDZ5bxIAAWv5MAqWOozCzs4IzsPcSWU9Quz1vsD3+p2aFZR70OKKvV0qHAaet27ZbKdsKf0rVKqlKVIE6BojkxTZrPFaG7230cbSYahJRWd7X4pBLVLRNkuQYkMny4chlupzGjjH/7zaQpccKZSFGXfahGA+Fjiv2/18wDcZx6S/WhrFZx4Q8X8zb/vy7+WOJlJqk/JEEvJ7SAfxEMRwEkCXG+WifOV+OmIxP5aqOnTwDg8a6pp7Lfr8x0A6rBP1HirlNRRZS0sqqUe+MV0bjdUpjt22ZBsVgw2j9KXTjvMcFo6CuK4vu+b5uFzqnHQu4aZD1C5FG55X76QfTqflvbPEDlllgbi/lntN8t/N/ns3qvygpYtBAPjnPgjOG6MpkCDyBwzGQWE3fAF96XUvPdir0zCL/wv97RdsaVW00JpUMrqxXb/jVCD6Z//lDffXztr2j9TpwxGb8S/pLm0C87jU536X91N+2LS123uwQACKw1vWvHfr1ZylS3ZIjLYthe5G3bvtK62zjugkV4/7L7GKgk3/m5Zg5wVWzrWGs6w7X18Pp122QH19bdf7wJ4P7fapsMdaMpAd1eK3qQIIa+Wnz/ZySg5w3Sn/MCLeBPm6yJcuBZumnqfKLJzT0nH1oCmdUt7zFfWr4QNZEr6nvXjzZ3s+dNRbZ2Pa4fTactnQSp2evLVliZr6jG3dvDjXA1HzgbJvOVlhHtz1sbpp3KkQKARsdtAOJnVF46S5Tw/dKvci3rSIJfb8Y7mraTeK3eUtnOjuV9wH8LhbtwpEZzDAESnZJhBYDS6hs13Zym3lTYKE8u4YOHD7hDZmrTh/Ws21sdKXA+H6Nej79wvY7B507wQbNsaDQc46rC9LBOYVlVDtjvPdykKShBEMfgWd3DZ0prvWZ3hUU7IFSkuEWnRHVLhqRmr49oKNVCdDIZT3iTxATDp8BFPAqVVb2lsp0HTnCzKfGxxcWvNjVtEwCwuIjHT7mSS82eK1ta/OWrrXsf0xSOIE4JWsCfNVKjY6jMZBN77Mdzof5Rr5vsMW/P+d6eE6nR6SXc6Lz9KJZZFBo040KDzlrJD0HiD5hThWAEQczM2+8A48mpXCB/zop84jKP+RS5Gk/2sk3n8YhquM2RpenRcfFs1FCZOMi39a1W3+3EV6jhYVsL22dWI8sOiIyKHL8MaeU9xbaTFiPcLEpGJDg/692TkmN7D4PmSvGQvM+gtO6mnxoWNQ0dBzlxELNBDibnFI/rR/Om1LyZlI3AaQPVILJyS2rdRJlhSRDddBl1JzkrcNomU1r9u9jQ7VrsaTQa+umFf+Ac7SQL89/8Sf8z8Z9Ziz7eOi/C6Gcv4CL9fFGQ/pwjaAF/iowHAeBP9J223n0avqjKClArfEIae8svyTVkWptSbaRJF/oJO9SiWOTn4GtM9kwh8JztLTvcglevsPRfj28fWO1dFjZVrV8PLV/gtHXbj/9ceJZmsmtlbvjEsZD1CDEFKa0Qky4e6xs4bV1P9rH4tr5vcFsjz9LMZPkMM8UIL2NnCk8CDpy2HtXRiE5MHhnMr54swGH2Xqvv9sTgXnSDogI0TyGlZs9tJt8YRf5KAZDxpUuXFuUOhWZ2Gb982n0nCOJYuCFozk671OylHgMmta5UdaccyohpuYxn78xtAgicybGihX/Y6i/aoUQGcKZBIJVTTxDE6UAL+LPGZ/zZq1xF6KhktuWwtly42PkTM0ferB65xIUeccWwfbGvFjk9zY3A+XQw+d4d3U4ky/mM+UCF+1bxZcLurp8Y3vSjxrCVVcX2mWnd6Hcaksgc/Wr8HD70BC3diRMSOBu2H/tritL1hH+c0rodbqbfUMES9e/RCFcpdS3hqGEPR4AUOFupTkw+lCzACU9K31LbZFMuornMiTspeGLpD0eQS4YAMFM7aoVN8E5b16wZ1/BkNkHMCDmYnEeC4dG0m+QJrStR3TJBTi2Q+ZJa1FEdK+PTMOGEx3s9h1Hsb19u66JQXomeysYkwk1mhf6v/sIg/Tk//MmLvoHXlMq7sfYCvm3qGkc3bT8xVY5p3G4p8ZG6mV6+V+oSIIkYucYNFQCz+TV1G6qam308C4FntXX70cT7o9+Nc44eD0fijuo5ows/Wol+CArATF3Tos2vShw1SpyEHxz+TbIBPvOSIPIZDf10iq+0sqqA7Xvh6+I2zvRI3iRWavZcVxjYtbkqpR8/5pzkWbrtq8ZUE0/P0m0fOcEWJyYVcbymgm05x0beEcT8KHrkSo9izxeepSVoxzKS1LoS1S0ZCoZHqQVyo+O6QvqOlfFpkJprKnx7Q9yz2FRJfC99b7Xvuq7ruv3VPV3TrOTlRVT85ByWOA+Q/pwjaAf+rFGqyjcj/9IPw5aoRsft4s49Nn4GALi4UP/4F/du5kxDpWav+92dezv8wIsV9ceXGPNFHXuyhD5wthgWlepjfwQoautu59qnTYY/fgk0ogMDeXvDZD6uCu9Rz+JR8FfLStdF6LJqqF+YLLVg5yZ76vIFxk32FEVd+1De6kabbFeAq0vLlyMPvvo1f/AVRtEPodeXLdNmIwBYAJ4s/zUt3wnizAiGR8gLAarN6fqZwvRqpnNyAmbut1oKihr0E/C4o1Q7aVXOfWKpyNWyISCddcfxk81IBEEQOUx0LkY56tku8QQlqjubIM9Lxhsd14Bm8usorX6/taHvydVodZ54ytkzhpqZlOnRMKeoniCIuUML+FOkUpdwmOlw5yXyyk8ieQucdjexGH72ZLC54Vznc9FKXcIhxL564LS7O9GBz8aMAcDlt+KPCo8EgDdXjc960WfUI1cofuAbvwnt4h+NobRuDNviJR6N8/u4OLJq9DsNCZ4FNn7n7czoBfmjnps02dsSXe4Sr8k6vGz03UZYnOqnd90kGSM+t4elmexy8Y/1OF5n+xMyICFyyc+Bb63XolpHuQZgfqnvaTzLZCnLprJd7fEgABbU283maGgfk0Zxv61t+gCftA5TWpsKK15WlUSlQO6Q9B/InftOeH0QxOlCDibnEJ6QPgyC4YY+bQ68VKa6xUPF6ll6wWLycuCrsgJwbd17+Nuaj9qaBHhDH1C/29aiPyXqektJPLnwfm4yiGgl8hA5n5D+nBeohP60YWaykErTTdtPPL/kiciKaoQFSUZLgW9vTxQ8BdYntg8svs+P7K+rCwCAS2/NcE8+YzWj77pu3+AF/DZa/LItBXzRnUuj2SlIfZLkGuDbG44nTs006UvNu4aqsLBTQLd9JV3v6lkmS1hWEwQxbyq33AT9FuzNuFexKivw9x4mfvU9K1X5+bwkN7e5A2cZl/9M4s71uXLIOew2N30sqH3XdV1DBTNFLae0slqBzw5Ug39VQz1gPipytXTo+qrCWzrdcAgAlL/6YF4/AYKYFpo9nzdE3bmuh7OpUGS7drGGlqhuyRDf7Y/L4gOnHZaxzyLjPAc+/Zalxd/DqNmbDPUbDXF9sB0W6yfbtKPNGM/SzMECoBp8yEwX1xMvF7y/sqjF8vulX/F/Z3xXxPTQDvyLYDgKIEmI89U6cb4aNx2ZyFcbPX0CAI93TT2V/X5lphtQDf6JEnedip4oSCurSrk3XhE8+M23zYJisWC0f5S6sJ8MgRbL9/ls/r3WThtkQEJMCTcmihDuwtF+t/B/n8/vZFVWwCIrpeA4B84YriuTKfAAAusfDp/h2vpWR7Th9FtHsXJ+P3mt7zP/mx1KGz7xxs/F9+/enO5OCWIuZObT/CVNo88nfGfjGRZq+SXlZapbMsRlMaxb97ZtX2ndbRx3wTzyc+CtHgOU90RkyPAIAL4ZBmhI0kptwfafLL4hqierbywCj/nXC5wthqvKk0eQqxM3SbxMkMi8GtAC/rTJVhEFnqWbpg7D7TTEHHqyahNIrG6BsMd8aflC1ESuqO9dP9rczZ43FVnHqNiEKp22dBKkZq8vW2FlvqIad28PN8LVfOBsmMxXWqHZfuBZG6Ytok/CMFJS+efn+6Vf5T5PJWkmgPGOpu0kXqtGX97SoylWo+N2/9DsRmqkrIfSxZ0z/iOe7C2Ejw7HgwD445eeZUbTQAW/jXLgL4ruS9EpmSjhv6rgkXiG9+UXwNPBg7a9y7OR1xcT9yjJVwCfme0/d3uppXTw4N+eAPhqU9M2k2//+rDTqDzcewRFXY57BpZV5YCJHPiioaYkNXuubGnCXxkXl7qffUyqRBDE8fAc+H5P3taSMzp1vXX0e9zmL7Ja1+81C1W3RJAhNXvdQTR0canrRNOnkrOyhFOvDwft7mHi/RGeAAt+JK3qegubtrCll68AF7Cra2LyubgIvClH5/7nN8CbJ/3REQQxA7SAP2ukRsdQmckm9tiP50L9o1432WPenvO9PSdSo9Nz48RTbz+KZRaFBs240KCzFv4QqryaNvMUg/E46dPpyCWI14y33wHGOS2J3n4qB757KIlfusBp65tt4cZRqUs49Df31+IcePYXcmTT4e/s/3WUA898+8FqnAMP8bAwnN3GOfB2uC/09jsAO/ztT5M58MKyA55lHuS3Ukoff+Z+nP4ulmayH//lUpgDf+PvEvnLnqWxo/BFyRD3nvIszWTqJ92lk/6cI15nJw5iZsjE5NwS5cBH7nUx0aPHrNaVqe60Q4HT1ruWl8iBLzorS5QDj8gkKUL5aWL2lZhr8i3+KN3TszSTCU+naOgzvvmfGxE/JaSfpweJzCsDLeBPkfEgAPyscZTaevdp+KIqK0Ct0Okj9pZfkmtIJ3OKNtJa9khp0g61KBb5OfgaOeZOgedsb9nhFrx6haX3+r99YLV3hVeeoq5f55YvKA5O/c953vFrAxmQELNAOfCx1/wccowJgniNoBx4TBMRTxDE3KAF/FnjM9tHqPR8Hmu25bC2XLjU+xOzVj41tjcc+W6zIWU94nJg+6Iwls9s50ngfDqYfO+ObieS5XzGfKDCfav4I4XdXT8xvOkD1daKxJ8C8yio1BWV1pz6b19LctfttKonCpkI/8kusp83B74JIAx9E+eVniRy4HtnmwOPOeUYk+UEMQtkYvKqkO4uT5QTTuTA56suioeqOTnwx19wej3LNNJP5MCbTGmJGoLAaeuaLearJUMnhf7ffoqQyLwq0AL+xVB5N/nwlGW83/Imj+UucakYOb7YZ7bO+KGKqipsTmv4wLM2TPZo4v3R78Y5R4/DZ7Y36mATi/6FH4V/Z4KHez5SRVlzmosTHPIsIcqhHPjovynHmHhRkInJeYVy4FceHhsRT7wMkMi8MtAC/qxRqso3I//SDwMh9I2O28Wde2z8DABwcaH+8S/u3czRO6nZ6353594OP/BiRf3xJcZ8UceeLKEPnC2GRaX62B8Bitq627n2aZPhj18CjejAQN7eMJmPqyI63rN4FPzVCnCp6N75JFxRDfULk6UW7NxkT12+wLjJnqKoax/KW93oye8V4OrS8uXIg69+zR98hfCv3WjoQ1mdpvqMIIi5kagVf3ly4AEASmuqHHghSXn3nYy54BPbOAe+eCjRy0oQBDElYQ48CqJ283hZcuDzSLXye1YqBz6ljlWZ58DLxUMgNSWI+UML+FOkUpdwmOlw5yXywvQD4JYjicXwsyeDzY3QcyS1rx447e5OdOCzMd9Tv/xW/FHhkQDw5qrxWS/6jHrkUcIPfOM3oV38ozGU1o1hW7zEo3HZ5FVWjX6nIcGzwMbvvJ0ZvSB/1HOTJntb0R+0qqyAHV42+m4jLK7y410vPoXGyGrrzAegqM9Tv0r2JxnIs4T48gtwZ8jkm4q67Kdy4NMuG56lmUfzW9JP5sDX8g8cDwJg8TsJkDqGyszCDs6oO0hsOUXVmrxs9PCXmh2W/dTr4I8IS4b4Nf/5nxjIRJM4IybbmsjE5Hwi6s637+NoM9EOqLS6q8UnlahumSCLHPiwLD5w2rrNZ5qzyfi330x6GiWanRR12YdqRDnwR/t/r5kH4jj1lsq3X6qyAvz7r/+7Zo7EN++v0c7My0lGZIhzyp+86Bt45WGmlkQ3bT9RZcQTkRXV6Luu67pu32gp8O1tL3uZwPrE9oHF9/mR/XV1AQBw6a0Z7slnrGb0XdftGyrg26aNFr9sSwFfdOfSaHYKni7zuFN7w/HEqZkmfal511AVZurip2D7SqJIfjT0AWaHDxHgM1vXrImfAUEQz0HllptgXfHZASq3xNpYaq6p8O0NR/wG8wDhHGu5WajKSVk7YQ48mJknBoFj2iKLyXVd11DBzFA1pOa7FQwG4Re+VRkMwralkiF4lqbv/qe4pFEjFSJOjx8c/k1yAp15+f3Sr/i/F3FrxEw0Ot2li2zTDpa6ocj21a/tru2julqcA1+gumWCzGVxSwzxHPjbjWMuWIj3L7uPM+/8XNN0+6mYFapfswNc+/Pw+nX47ODauvgrco3tMNTXmhJX1tHhI/HtjSu2Psc/IcSpk5Eg4uWHduBfBMNRAElCnK/WifPVuGvohOfI6OkTAHi8a+qp7PcrM92AavBPlHi7fPREQVpZVaadWqcpb9EHgtH+UerCflxsFgyPAKRS4nWTsVl7p8iHIwt5lhBATg78LZXt7FjeB3wNLyyEo/7JGe2H8sjkwCutvlHTzWnqTYWNsmndyNxM8PAB3+hJdXyynnV7qyMFzudj1OvxF67XMfjcCT5olgzB2WKoLr054pHIPO+TOjgJgpgGz+oePlNa6zW7K3zYAWBZVQ5YsQNoieqWDEnNXh/RUKpW6GQynrbaEwTDp8BFPAqVVb2lsp0HTnCzKfGxxcWvopD4xUU8fjoM0IDz+RjVpauH0bdfAK5M5ctPEMQM0AL+tMnKZ+BZumnqMNxOQ6RzTFqOAJmpLe8xX1q+EDWRK+p71482d7PnTUXW8jn+6yLJNeBolmtKzV5ftsLKfEU17t4eboSr+cDZMJmfWqJvmLZuyW6nkbSoFpfiU+d0IRgxM+RZQhTkwAfy56zIJy6jEopcjSd72eryeEQ13ObI0vTouHg2aqhMHOTb+lar73biK9TwsK2JDXVjNZlK7G3bfp5LvbTynmLbyQ4lkTuP6MHoz3r3pOTY3sOguVI8xHs4//Kf/qkrRibLUaeHGnmIEqit6dXD2+cebjel5s1kDnzgtIFqEDWCJ7VuorK9JIhuuoy6k5wVOG2TKa3+XWzodiIHfjT00zPXwDnaSSrhmz/pfyb+M7boG2X1M1JW0s+XEJKgVwAqoT9rpEbHUAG2f/LazAv1j3phaVav17n+p6dwe8+D1OiE99frNCQexixLcaFBMy406KyV/hAKvKIJgpgjvPVlyLey+VSMl026/RZsve3EzTS+re/f4L/dhurbeqK4nJlihJexa1ty2JAT13KKTsywsrSF1BXA7L1VPthrJiecvALUcHM6OKVmz009kIgFB0Am3I6bO+HYodkmyQRBvOZ4+yw/a1Nq9jpNKX4/qXVSmepOOZQR03IZz96ZmyetQLESihr9sB3K27b9hL0J6SdBnB20A3+KjAcB4E8YR7XefRq+qMoKUCsscYq95ZfkGjKbQSkfqKQL/USeiZjZzvGb4WtMmp4g8JztLTvcglevsLSef/vAau+y0BVl/Xro2Qqnrdt4f331Dw/Ck5X65QlDU+I5IGMk4hgCZ8P2lVZfCIwoXU/4x8Xhlo0bKlii/j0a4R05Ydcj94sbjgApcLYYVCMSr8lw4rxWycBpm2zKQn5uaCfupEDw/NAqOX9oJbvhzgukZjNRpuYUogxqa3rFCIZH065fE1pXorplguxt236kp9w9T9RRHSvj0zBRepRSQlGjL+rklVbf5YeVn3VC6Lfg1CEJOv/QDvxZ4zN75yBUemllVQEz25H3GwLPaWvapHdS44aadInLeMTlwPbFY9fIqnluBM6nE5nugXNH0007tKGDz9gBUOFPZvk+3+5uNAqfbe6Gzi7SyqoCf3czcbI/GAD1G3NqwH1N4ZYkZIxETAWPckxYLXFxiotkits40yN5k9h4tzxw2sLGMnVAzkmepdu+akw18fQs3faR8MWchYz/k2fNWTcJIqRIhEmcXxk8K+lenNgFT2pdieqWDAXDoyg9BOCJb0L6jpXxaShVQs/SND2sIXD7q3u6mK+Sfp4rSIJeAWgH/sUQ2h7zh6cs4/2m5KSolbvEpWLk+D4Ys3XGD1VUVWFz0tLAszZM9mji/dHvxjlHj8NntjfqYBOL/oUf8b8zknw576O+OVmkKhGR6XjnL0mXiRKC4VHGD45Tm9P1E2ZJquF2qlHnZAHM3G+1FBQ16CfgcUepdtKCDhxFrpYNAY2Oa0Az+Q9BafX7rQ19j4pCCYI4honCxyhHPSjWuhLVnU2Q5yXjhUoYOFu80z/shuT2pFymST8J4iyhBfxZo1SVb0b+pR+GniaNjtvFnXts/AwAcHGh/vEv7t3MmbFKzV73uzv3dviBFyvqjy8x5os69mQJfeBsMSwq1cf+CFDU1t3OtU+bDH/8EmhEBwby9obJfFwV0fGexaPgr1aAS0X3zifhimqoX5gstWDnJnvq8gV2IIrg1bUP5a1uVCV7Bbi6tHw58uCrX/MHX4H/tfP2BymHPkVprcX98gRBnBLePgu3cuQagNNKPvcsk6UsmwqaMmOU1u1mczS0TXvDWSm+KSFJefed8rgPhkeJSoGSoWjaza/uo7ZGQkScAtTW9GrBE9JPtusglalu8VCxepZe8GTkK6E3nGhurMpK/OSC9PMcQRJ03qEF/ClSqUs4zHS4B56zYdrKT6Iap8BpdxOL4WdPBpsbznWuv6l99cBpd3eiA5+N+Z765bfij0FFODIAACAASURBVAqPBIA3V43PetFn1CNbZ37gG78J7eIfjaG0bgzbYQb7o3FZ77msGv1OQ4JngY3feTszekH+qOf+XfxVna3oD1pVVsAOLxt9l7vQO23dD0vJ+CLiw+68Iqtec/9SMhclJvnyCwBswo9j2Y9KMSct14Xv3LyW9MnCzpSBR5bxIAAWv5MAqWOozCzs4Iy6g8SWU9Quz1vsD3+p2WHZT70OKKvVVGM+AqetD9cM+Hwo/tbhT6kCqAY18hCnRzRppmn0OYdXU9rb93G0mWgQUlrd1eKTSlS3TJDlGsBiC5HAaesikGM2Gf/2m0lPI17ZxL+DuuwLJazKCvDvv/7vmjkSX6+/NkwKKOnn2fL8ukGCc36hHvjThpnJTihNN20/UYAUOBu2D0U1wo4io6UkLD5jAusT2wcW3+dH9tfVBQDApbdmuCefsZrRd123b6iAb5uxn6kCvujOpdHsFDxd5l3uUYv+RJO+1LxrqAozdfFTsH0l1bCqyNXAc6y26BdrW96xW3QEQZyIyi03wbriswNUbom1caaDUfi/51jLzUJVTspacJyBR0zjdksBMyc8QQAEjhlmMcUO+OJAqfluBYNB+IVvVQaDsG1Jaq6pYKbl8b75r/9n/DU9S9M0k6kG18IKxsDTyQ8miHmScSrJvCTODY1Od+ki27SDpW4osn31a7trh3Y/k5Sobpkgc1ncCrvNt20/bLucRca9f9l9nHnn55qm20/FrFD9mh3g2p+L679bwejwkfiKxhVbjz6A9PNMId0gaAf+RTAcBZAkxPlqnThfjbuGJv2ZAQCjp08A4PGuqaey36/MdAOqwT9R4u3y0RMFaWVVmXZqnaa8RR8IRvtHqQv7YbFZMDwCsLeh+7HFnc9Mfea9v9fdQpPMRYl8xjuatpN4rd5S2c6O5X3A1/DCXTjqn5zS/30aRKdkWAGgtPpGTTenqTcVNsqmdSNzM8HDB3wPKNXxyXrW7a2OFDifj1Gvx1+4Xsfgcyf4oMldlG99oZm8GmUwiL9mVa4Co6hQQTWMd0xzywmOacInCIKAZ3UPnymt9ZrdFRbtALCsKges2AG0RHVLhqRmr49oKNVCdDIZT26YRwTDp8BFPAqVVb2lsp0HTnCzyZW1unT1MPqKC8AVXktJ+kkQZwot4E+brHwGnqWbpg7D7TREzsak5QiQmdryHvO4TRyKor53/WhzN3veVGQtn+O/LpJcA45muabU7PVlK6zMV1Tj7u3hRriaD5wNk/lKyxCt7YFnbZi2bsnhz8b3Iarzw1HmnzT7hAAAfL/0q9wHsVQo9Trz9jvAeHIqF8ifsyKfuIxKKHI1nuxlm87jEdVwmyNL06Pj4tmooTJxkG/rW62+24mvUMPDtiY21I3VqOMHImg4x6VeWnlPse1khxI3i5IRPRj9We+elBwTlaWB094ZK63+XWzodi22uQ8e7o3S3yxwsvWoU/OaN/IQU0IdT68M3j63d7spNW92Eu8HThuoBpG7XVLrJjYpSoLoZstYLzsrcNomi5QwbokcDf30zDVwjna4EPKhv/ynf+rG1xDKCtLPs4N0gwAt4M8eSUxkJ/bYj+dC/aNeN9lj3p7zvT0nUqPTiw1M4O2HdidhoUEzLjTorEU/BG67ElchQGp07raOdPuEhjAEQZyMlHUyXwCLWN/Aaet6G/FszLf1fYMbFHmWZiaevoGZYsSzNNPUmNLquy5vMY8s6EQnpogMDpy2nrwCmL3X6rs9MbgX3SCvAM3dQ5KaPbeZfGMU+SsFQCbcLvlkMjwxcHJ+IoVnEQRBFMK9fHJ22qVmL7meT2ldqepOOZQR03IZz95ZoRLO+CSB9JMgzgxawJ8i40EA+BPGUa13o76gqqwAtcISp9hbfkmuIfMsM+UDlXShn8gz4TPb2hy/Gb7GpOkJAs/Z3rLDLXj1CktL/bcPrHYYBa+o69dDz9YLf7iYF3wCXOTfhzgpZC5KnJjA2bB9pdUXv3GidD1RBROHWzZuqGCJx2vRCO/ICTsuE35xgbPFoBrRbzMfSj7EzGvTDJy2yaYs5OeGduJOCgTPT4riJLOdVQC1qxBTQR1PrwbB8GjaTfKE1pWobpkge9u2H+kpd88TdVTHyvg0TDjh8VLR4ah0CKSfZwjpBkEmdmePz+ydg1DppZVVBcxsR95vCDynrWnahGlT44aadInLeMTlwPaFjUlk1Tw3AufTiUz3wLmj6aZYvQPwGTsAKty6hVvc7e5Go/DZ5m7o7FL5L7kx8MDl/1LJHyCm4fulX/F/CA1OyOOEKGQ09FNG8UKc9iMlKm7jTI/kTWKlZs/l6/DAaQsby9QBOSd5lm77qjHVxJMb0qV8MQniHFD0dJWeup53PCvpXtx2IlvepNaVqG7JUDA8itJDAJ7dJqTvWBmfhowTnmfFM8iSIeIMId0gQDvwLwrhiCwenrKM91u81xVT7hKXipHj+2DM1hk/VFFVhc1JZnl/+qOJ90e/G+ccPRYPXxs36mATi/6FH/Hl/cef9f+0nZ3RY0G99zHNxp+bzKKdvySVJzIEwyPkFcLU5nT9hFmSaridqtMusLsUMHO/1VJQ1KCfgMcdpXovq7KSd+Qx+2OznUUQBDFR+BglogfFWleiurMJ8rxkvNFxDWgmv47S6vdbG/oeF8LiIdJPgjhTaAF/1ihV5ZuRf+mHoadJo+N2ceceGz8DAFxcqH/8i3s3c2asUrPX/e7OvR1+4MWK+uNLjPmijj1ZQh84WwyLSvWxPwIUtXW3c+3TJsMfvwQa0YGBvL1hMh9XxSa3Z/Eo+KsV4FLRvfNJuKIa6hcmSy3YucmeunyBcZM9RVHXPpS3ulGV7BXg6tLy5ciDr37NH3yF8K+dcMDbYpETfbX1iw4t3wliPuTnwLfWayzcyuFWFHNLfU/jWSZL+RuVpUSOBwGwoN5uNkdD24y66HO539Y2fYBPWoepavuj/b/XzAPxYllVJmsI8tKPo84Az9LMf1+qAj8qrDwgiDlAHU+vBFWZNwUGww192hx4qUx1i4eK1bP0gsXkKWFVVgCurXsPf1vzUVuTSocCINLPwGnrw7W+fFRWuUU8F6QbBC3gT5FKXcJhpsM98JwN01Z+EtU4BU67m1gMP3sy2NxwrnP9Te2rB067uxMd+GzM99QvvxV/VHgkALy5anzWiz6jHtk68wPf+E1oF/9oDKV1Y9gWL/FonG/EwpGFVbxngY3feTszekH+qOcmTfa2Ij2vygrY4WWj7zZCCxY//WhWanR6DeGDxZSWMfs6gvxLI8iqlEhQueV++kH0KnDa+ma0fp9sbgx95+a1pE8WdqYMPHK5/GcSIHUMlZmFHZyH3eamjwW1v9WRhIEeT/eQVlYrts0Owv55/uSxspraCuLpx4kunUTPvigNvfp4BOX/pQ0kYt5MTrtp/n3O4dWUtq5Dicw6EThtvWtfLGwCL1HdMkGWa0DCBzlw2roI5JhFxieUMJqG8e/B5bNuNMqHYv0c8Y4mUc9P+nmaJHskQTLymkE98KcNM5OdUJpu2n4Uuy48TKCoRt91Xdd1+0ZLgW9vT3QsBdYntg8svs+P7K+rCwCAS2/NcE8+YzWj77pu31AB3zZttPhlWwr4ojuXRrNT4ArPu9yjFv2JJn2peddQFWbq4qdg+0puw2rgbLF8QyuCIOYJdx+KyDQ3Cv/3Of0mVuWkrAXHGXjENG63FDBzwhMEQGD9w+EzXFvf6ohSpn4r0ez5/eQJibc8S4t35yOk5poKZlpe2Nn5yCctIuZLxouErEleafiO+DMs1FZyZaREdcsEmcviVtiIvm37YdvlSWU8VwkDq8cA5T0RGTI8AoBvhkH5UKifmmiW+vp/zvEvCFEA6cnrDO3AvwiGowCShDhfrRPnq3HX0ImQudHTJwDweNfUU9nvV2a6AdXgnyjxdvnoiYK0sqpMO7VOU96iDwSj/aPUhf28kDhv2/ZzHQBOAplwxpBVKREz3tG0ncRr1ejLW3rUZt7ouN0/NLtR/6SyHhYP8cab/4j72BdC5RkPAuCPX3qWGZkZKfhtlAN/UWyzS82eMdTMRAn/VQWPhAR8+QXwdPCgbe/ybOT1xcQ9SvIVwGdm+8/d3s3klwke/NsTAF9tatpm8u1fH3YalYd7j6Coy3HPwLKqHLA4B95kSqv/4aDdPUz/hER/Z1icsqD2T2LeTBDEawvPge/35G0t2YOurreOfo/b/EVW6/q9ZqHqlggypGavO4iGLi51nWidXHJWliIlHOEJsOBH0qqut7DJw95LhqRkbT2AwWDKBBGCIGaCFvCnTVbDAs/STVPnpZ58EywvQi2zuuU95kvLF6ImckV97/rR5m72vKnI2orEXUqzB3eKNnYzTJEz7t4eboSr+cDZMJmvtAwRBR941oZpp4KggTBJlR7Zzo3vl36V+ziW6qxeN95+BxjnzKe8/VQOfPdQEsWWgdPWN9uimadSl3Dob+6vxTnw7C/kqMvH39n/6ygHnvn2g9U4Bx5Ca8LZbZwDb4cxSG+/A7DD3/40mQMvOn7gWeZBfg689PFn7sfp72JpJvvxXy6FOfA3/i6Rv+xZGjsSZ4bpx4g6ixJUZQXKar8nb2vmkTzDj5pDjTzEJNTT9OoS5cBH7nUx0aPHrNaVqe60Q4HT1ruWl8iBLzorS5kSKj9NVN0HTnuqoThYvq3btekSRPIh/ZwG0pPXHCqhP0XGg2CihF7b2JdvLWO6XI/xIACCQdj4fqH+Uc8V9Hqd639aeOSpE+bAZ5BrwofUP9p/+NuhL54N8EKD968Pt8IS+i28m/khBI711yYDwEytbXllJlcEQcwbESAcdvdIzbstJdXMk86BTzbapHLg40dw0sqqwlOAeWtM4tHcZLbR/HPgJ+H3csxlNmzELU4EQRBT8Fw58HmqWybIPAc+1NnmmhrV0x8r49NQlRX4ew/jSdho6Av5LBkiCOJsoR34s8ZniRr1qqwAtSlmqNy1JOVNUugDNZFnwjU258gZycuBBzxLT0SC+szeROoPWrC76yeGd/yLseUzT4JKjJr6HN2zXmsyVqWcHxz+DW3CEylGQx9KKxMgbCeaeZ43B74JIP2rXis9SeTA984wB54v3/vz0B1qUSFyoJ6m14ZEcCaQjOCYyIHPV10UD1VzcuCPv+D02+HCjy/M/8jmwBcMzRX6dZgK0pPXG1rAnzZp52cE93/+yebgCSpyFaG4mm05rC0XLvX+xLYTb1a3Nxz5brMhZT3iJl3owfZFZyvfmpoXRTnw9//WZMBCff0X925KCLx/7pq7Y+AHlyQAknwZeAZcu9X9xw+WgOC+9ckme4LF6/wPjbdt+8C1Cr661Or3VkbOhmn7qacVxPMxWUtPnqVEEsqBn+fynSDyoJ6mVxbKgSfOHNKT1xxawJ82WeMozsI7K1H1k22yjPdbno1bgUtcXg788H8DALN1xg9VlpWLByIx/rRy4P/4DACeDDb12FJqcRFf/d7DzQYa/62OwQBf7XRTPwvpv8ZzZUX9M599pcoSJPFDSdYQEAQxf3jf5kuWAw8AUFpT5cALScq775RLZjA8Oi6ROHi458P3U3NfW9f2qBSIIIjjCHPgJ6x5S3hZcuDzSLXye1YyB75kiCCIM4MW8GdNZXFh/PjKT2+nNovSHZvV1fzIkam5DCwq1cf+CFDU1t3OtU+bB6mP+Ob+hnkQ5sADX++ncuAXhkUXDnPg//XOxNAFoKouX2D8soqirn0ob3XtMAd+ScHAr1Qw5uv+xQU8fqIsice2VVkBY1+EW2Q8S266drIcyP5kEjI7ec358gsALOECDwCKuuy/hDnw40EALH53fA58VFwktpyiuiVe2XT4S80Oe33qdUwmEn/7TfQEFKLMP133CvLUJOZMpqeJ9speFURx+fZ9HG0mqiOVVne1+KSXIgc+q4SILyosk9VlH6rRONmQb1o3yIX+tCE9eZ0hE7tTpFLPkczx4yfH5cCPQs+RSl0CJF4Yz8vMlZbIgTdavEj08lsTRwIA3lw1uNldpynx0Xfejg589tVBIgceGLBUDvyVIu/lKAf+7XcQXpAjyTVgxL5eFV+kd/fG8LOJHPhxtGv/+EkyB56PPuET8ZKUeIIgZqdyy02wrvjsAJVbnXD9et5y4B3TFllMruu6hgpmauJAqfluBYNB+IVvVQYDVN5NfxfvX3YfZ68pvPLF9aC0KEaOOA2+X/oV/zc5xJOcKcz5/NHodJcusk07WOqGIttXv7a7tl+4KfPCc+CBPCX0fq5puv1UzArVr9kBrv1547ihuJup31KASkKQidMlEhOSjtcK2oF/EcyQA3///2MAlOsr/EipKl8BfODSW7PcwMuWAz9dSvyUkHtHDmR2QkzmwN9S2c6O5X3AV6mNjtuX23pUQz7HFN9MDrzS6hs13Zzml1zigji5mRM8fMCdj1Mdn6xn3d7qSIHz+Rj1evyF63UMPneCD8REdmKjXVzz+EJ7gjgtyKbkfONZ3cNnSmu9Znc1LXp3WVUOWLGolKhuyZDU7PURDaVaiE4m47lKGAyfAhfxKFRW9ZbKdh44wc2mVDIkvPGNXlMKHADvrLcubZ7QPo+YDZKO1xNawJ82c8qB5z3m/q6uJaLfLwJXZrmnlywHfsqUeGJ2yOzkNacgBz6QP2dFPnEZlVDkajzZyzadxyOq4TZHlqZHx8WzUUNl4iDf1rdafbcTX6GGh21NbKgbq8lUYlF6NNFQI628p9h2MsJD5M4jejD6s949KTkmKksDp20ypdW/iw3dTqUfj4Y+1DURiRc7OxMEQRwDL9+527wpNW8mc+ADpw1Ug8jRJ6l1E5XtJZ2DszUVlp1VpISjoZ+euQbO0Q4vzC8ZQuyNz1NHAqd9YlcAgiCmhhbwZ40kJrInfTB5AaguLV84FM3siqK+d/1oc/eYs84UqdHpxd4m8PZ9QI1z4Fuh0z4gNTpr0Q+hfPQFfA+CeE1IWSfzBXCr74rwCl1vI55g+ra+b3DvIs/SzOTzNWaKEc/STFNjSqvvuhK/Xpw3pJlH4tri6skndMzea/Xdnhjci26QV4Dm7iHF0XQCPreUJWHylNr0Sj6ZDE8MnMwVg+ERFGxpmnCFyPXGmxJy4iBOBNmUnHO4IWjOTrvU7CXX8ymtK1XdKYcyYlou49k7y1dCzPIkoUSQTw7p5/SQdLy20AL+FBkPAsCfMI5qvfs0fHFMDnzsLb8k1wD2+P/uu3+X3FOKfKCSLvSnngMP4GtMmp4g8JztLTvcglevZJzovn1gtXdZaH2yfj30bAUA/OcwHlXU1oczOLoSpZDZCVFG4GzYvhKlqInS9YR/XJyN0bihgiV+O6MR3pETdlxyK7nhCJACZ4tBNaJZ5GQ4cV6bZuC0TTZlIT83tBN3UiB4/jHBFqOhD19dc3vxTFgbzq+PgCCIV5VgeDTtJnlC60pUt0yQRbF6MxFlJOqojpXxaZhwwuOlosNR6VBaWVOCTBDEKUAL+LPGZ7x4ar458JOcUg48AATOp4PJ9+7odiJZzmfMByrcuoU/Utjd9RPDmz5Qba1I4AFWjO2O4kG7C0BpPacZPzHJ5LqdlvQEwCdiqV+57CK7uI0zPZI3iY03ZxIuxcklds5JnqXbvmr0ppkAepYetl9OcXQRqXSkzMT4pJC1BHEyyKbk1SLdXZ6o5klqXYnqonioGherA0i1+xwr49MgDPXj6qn4i5QMpb/8cwoy/X/+BJB0vK7QAv7FEDoiP3cOfHi9uoRDCBd6vthP5MCrqsLmtIYPPGvDZI8m3h/9bpxz9Fg8mG3cqINNLPoXflS6Qr9MZlKnCxmfEBHB8CjjB8epzen6iemsaridqtMusLsUMHO/1VJQ1KCfILI+jieLVVnJO3KmJtLjtu0JYh6QTcn5ZqLwMXocGBRrXYnqzibI85LxRsc1oJn8Okqr329t6HtcPkuGxD1MCjJxmpB0vLbQAv6sUarKNyP/0g9DT5NGx+3izj02fgYAuLhQ//gX927mCJ/U7HW/u3Nvhx94saL++BJjvqhjT5bQB84Wm8iBZ/jjl0AjOjCQtzdM5uOqSJ7zLB4Ff7UCXCq6dz4JV1RD/cJkqQV7MPw6Pwc+fPJ7Bbi6tHw5auGvX/MHX2EESPxvTlV9/0dHcYH9+9ePdnf3PVAPPEGcHrxv80YDvA4GpzXp8iyTpVrKg+POUFq3m83RMLHZU3BhLXPliFQHDvnLEwRxilRnaPuTylS3eKhYPUsveDJS9Uie5aO2Jh0/VCLIBEHMF1rAnyKVuoTDTIc7L5FXfhLtPQdOu5tYDD97MtjccK5z9UvtqwdOu7sTHfhszPfUEznw0ZEA8Oaq8Vkv+ox6ZOvMD3zjN6Fd/KMxlNaNYVu8xKNxme2IrBr9TkOCZ4GNkznwAIAL8kc99+/ir+psRX/QqrICdnjZ6LuN0ILFT22IXZDf63Q+7sTntndn7YEn+5NpIOMTIrl+R1WugOUFYgDK+++XXubpfrs9vHvcjE1JRCEHD/fSXepf2xmrpZosAVLHUJlZ2MEZdQeJLaeo8YiXjR7+UrPDsp96HVBWI72J6wH8OKAucNp6bGvvWZr5b4upSlXiZeLV6/0hm5LzjCgu376Po81Ee6PS6q4WnzTRUh66ffaaJUN8tz8ui08oV9lZxbfx7TeTnkaJZidFXfahGo3jhtKCTKv4FwZJx2vCn7zoG3jlYaaWRDdtP4pdFx4mUFSj77qu67p9o6XAt7e97GUC6xPbBxbf50f219UFADPmwP8f9t4uRI7syvddPoMKrBbjFn2lfIiM49wNRwMioaGQFZRM3SHuSzOCjtE1IT0UdJgWFlaMH+LkQ9eDww/54NBDeSBvPthZRmZMx1zqQFXA0eSA5uplNkb3lAidppiBPMKjAUd5MjfcLDGobSQbNA99H/aO74/Myqqsj9b60TDOXDsiIzXUyv2x1v8fUNqwe/1+v2erAIHruGDw2xoE+KK7EEVvlSyoJbkBELhrni8uzTTpS/qqrRLqmOJfwQ1I1B816VoEQQ4fvohVbbE2lvT/vQYA51SRimwVAFS73+/3ux/+aeWdfksn/bXW5WRaY/m/7wslkz3llkGAOp1cQgRgnhN6MYnnpY4mBkr6lRoMBrWb/LvcrA0GUdsS/+KqzbNdsxldJS0tE6DbPh/jUIDXL+sownTy+NrO95Mlo5mXp50vF3/O/zvuB0H2idJqLy7QdZcttvuCnvrCbbsB1JeL2wUlfUWFwF3zxNyHu26s6FJlSKTFDRHyN90gbLusuqoM/5cPXmbe+aGmme5rMStUX9Cn8P6fKRNCzGu7AcBVW/yANFxTK0rcyGHyFct+yL7AE/jjYDhiIEkQ+6u1Ygc1rhqa0xwZvX4FAPDygWOmrONm8oEH1eafKPF2+WhHQVpaJrMtnatb9AHYaHs3deMgPl+fdO0+Qd2OqUDhk7cOmnHEyEhlXroMMAZqarQwfBAkvWsPNSf8fGL07IbpTFNiI2SU43PyEPbkEZe9THV80m7n1kZLYt7nY2g2x1uatgUAAM0mDD732Md6KIjfUoANAeDCX9rqQCjVSXq3L3c0TQvv9q5h4xESgiDT4HfaO2+IcbfhtuMcAldV8pSWd+8orX5PtswohyWybkVI0rs9iEKps+6KqwqfWcvL0LHha4AF2Aszq3pTpVuPPHZdl8pD8OjvxwAAT53El6fOHbl3H5MogswDXMDPm2z6ZH7HdBwT7H5LERYcRXWrmakt7zE/LB/4rJhT/OuStEveH5Le7cmdsDKfqPbqreFauCJn3ppDA2KEYvvM76w5buRdWnktMhdQ+OStIiOwXs63iqZ7oYh8arYX6zWNgScx1e63+n1gnqWF24DEWA1no7cMQh8v91aGpkNd04ltJpRW3+5ocQk9r0WNNhuEq3zumZY+JK6b7FDiYlEyRBujP+jeS5puuo+fMH3pyeNAFNOLb8W8ZNGpmP4+XjYarrs7zb9YMdjIMyew9wc5mfjbFIixql+X9OvJXMs8C6DOInW7BjwRCbKgyHz/9usTqLqKeZZDidFbhTXTbcQtkdzAPZVad7d4jqwI3f6sfztzd9NtfG+21Tvmz2nAZPiWgwv4o0ZSWrZKHbo/Xw8AADjT/KTbTvaYW4f8bAdEUlrdxCrB3w5EP31YaKDHhQatlfQ/Qum1CIKcBFKu7MyzTMeSe11+aB03WfodzaHE6PWFhaWZ6m0PXHPb5lsJfkdzoi28JKJns8+vYZ5lFg5LWNMJ+NxSloTIU+rQK70zWREC5q25YPR0eRN3EBEEmRIuKFIwZ5H0bmrvlLqPjV4/VCji246FCXPKUCZFVlxV8GR9HQCYl48ddCdBGNKvYgcSgswLXMDPkfGAAQSZslWiGldehy/qMgFolJY4xdryi3IDMtokKR2opAp9zs+Ez2wbh/jN4AXkRU+A+d7mhhseo6sXaCrV/8fwUcd6EEaN72Y0W7k/nZCwb74TzLrhjEwNaiYhObJl9hCdt4yGAagrcQlnevXMYd4GP4WSxKhV47GZ1JGPHTKVayrQfBG9qHCP0ty0NsZcP0ncviThBcMRyOUhkOLluwQHbN7EPpR5gb0/yAmEDXennbMkWtLFMreXTpibvt5SKkLgb7pBlCV5xZLw26y6ampySni8VHQ4qgxFeTws1IofY//g3/JUYDJ8u0ERu6MmoO7W0zDTc9kkx4r024D5nqVpee0P5ZqaVHqbrPNGt4WMSaQMemgw737O0515dzTTcSM1q4DSpwA1Lt0iyQ2AEX2QiLptNwh1qZlnaZrpxNFgMIiuRRDk6FDtfhYx9avLBKhTkJsSjIZBSm2eZ7hgOApfT3Ryk/RuPzrkF4KXk5/a75huAJEu5qzw5fsqtmyeYMr2GXH/ETlR+J2kerHlRcrAyVV+ccKk235liA13U/YYSqvfF6mv4qrpySjhcUXPiaEIxjc60wAAIABJREFUpRWr2CW+N3LoYDJ8y8ET+OMhVETmm6c0o99GCqSPq5XeUjZyXJmOuiblQ4mqEnpIa3h+UL6Xe3/063HB6HFqYzbHO3w2L71/fgGCN5ngmYM8JjIFmR54/hJTP1KMpHf7S55luuEZ/SGK3CVJNNqrdr9V96xqOQzuaZRqJ63LpGgkketVofj0HUEQZD/kCh8j2RFWnsHYcDejwslpVIYqmO2qPEqrb4Pm8PsQo9cz1szHfOOhIpS5xy2DUDdVNoogyCGCC/ijhtTJF6Pg7NdDTROl1W/DnXt0zBewC+eat39y73pBwpP0bvuPd+5t8YELNfXbZykNRB17soSeeRsUzpP6y2AEQFRjtfX+fZ3CH54DKNFAJm+uOTSAi8I63u9wK/iLNYCzZc/Op9ZEtdVnDk0t2LnInnr1DH0qquDVle/KG21e+8qGuwB19cZ/2Q0P4Yl644PdBw+2fVAUYL95+QYukvpeMBLXfvjB7voDzPwIcrKIKucZX8nDxDX8ftt3/I5DU+JO1Qc4IiUVmdClyvPZcDdx/F8YYk8eBxAEqbmva2qP0c34pIG9P8jJo55pCpwGSW4AQEmGKQ+V58TKG+6PlO6p3wmgsSJNDuUehlYf4iAHBJPh2wwu4OdIrSnBTqbDnfnemuOSj6IaJ+ZZ7cRi+M2rwfqa9wHPv6lzdeZZ7a1o4JsxP1N/5734o8KRAADvLtufdaPPaEqww0VG+cBv/CrsN98bAzGuDa2wgH1vXCUeJ6t2r6VI4HeAji9fykTPyJ90+0mRvQ3xgyaiH7Zat1tx1Hogfu64+NTKz34R/zv5nfUHs2Z+1C+dBtQvRTI8fwaFPfBF62PRW8lfvAZgfPMw1yE5ZWfo82cAL/ktAFIloCmpjwxRd5A4copKAnjj/M5PNTfs9Wk2gWvPl4ckpdvXc65KEyyUkWMAZ6vIyYNXU7qbD2F3PdH2Q4z2cvlFuYQZanh29YoQXxnHwiBc793ut5SqG1bksd9/kdc04pVN/DuoVwNQbaU6lHgK8bH/dB6ljOZHMg1iJnw7wR74eUOdZCeUZjpuEIs8cckRIKrd4+2mPdsgELibuY4l1vnUDQDO3+Aje3fVcwAAcPa9GZ4poLRh9/r9fs9WAQLXccHgtzUI8EV3IYreKtldluRGskU/06RfHS2a40e72QiCHCG1m7ke+H6kL5/oaOTOEnIdAJ7/Nro60yHJ09sUa+BLlwGkZg34X36cAFmF1AfznNCLqd/v920VqBM26Ev6lRoMBuGXuVkbDMK2pYpQ6AQV3g+I0ZtHkwAyK1/b+X6y8SfzEkGOE6XVXlyg6y5bbIeps6e+cNtuAPUSRZ9MwuQFSDxhVoRAuWUQoBthI/qmG4Rtl1VXleH/8sHLzDs/1DTTfS1mheoL+hTe/zNlQijZbc9741++rKOiyBzANIhw8AT+OBiOGEgSxP5qrdhfjZ9s5VSXR69fAQC8fOCYKe/3CzM9gGrzT5R4u3y0oyAtLZNpNKPyVLfoV0cPGZTfnArUL0UKGG9p2lb2TWL0urrS6hmWGVeYR+fyl74JMN5pa5pq91vCRz0cRiI/uCmR9K491CIXeGL07IbpFFSmsiePuDRequOTdju3NloS8z4fQ7MZf5lmEwafe+xjvSqUKbRHEASZGr/T3nlDjLsNt61p0btXVfKUlieVTMJMKotUhCS924MolCqRqriq8Jm1vAwdG74GWIC9MLOqN1W69chj13WpIgQS9xSNv/v5GzYu3xFkbuACft5k0yfzO6bjmLx5lFtw5CVHADITVt5jvnj1zM5TUbkkGsVneqZsVVP865LxRN4Hkt7tybETnGqv3hquRev16ihy9Hy5+PPCXVusxXpruXQZYFw13eOVNOGrhiyliimngcj1eMZY2LkOAErLVqkYE7jmhtHrtwrclZc+JK6b7FDiYlEyRBujP+jek5Kxx0+YvlQe0qWkU57S6ven/l55sJHn0MGuH+Qkw8t3VvXrkn49afzOPAugzqJ+wAY8sbSweCibAQ9qv76vq5hnOZQYvVVYM91G3BLJmxpTqXV3ixfmV4RCTaeebJmPl42G6+7+6QwPzMH8WQamQSQCF/BHjSSmp5OdjXOcaX7SbSd7zK1DfrYDIimtbqxtAv52kOynL48WbBrwnwk8CkOQkwLzLIeGUzfmWaZjyb0uP3SJmyz9jubQ8NydeZZpWhDPUgPX3La5AJLf0RyzI+e3C0TPpji4Z55lFg7LW9HHSYMBZE7S0zuTJSE23AUCG5ompkFlOwwIgiBp/G1aPGeR9G5yPQ/UfWz0+qFCEd92LEyYU4YyKbLiqoIn6+sAwLx8bNadhNDLQ97E8xkEmS+4gJ8j4wEDCDKiUEQ1rrwOX9RlAtAoPfOKteUX5QZktElS6k5JFfq89ud+laAn8wLyoifAfG9zww0P2dULNJXqy6Mp3RW/ozlw19hF9ZO5g/qlSI4CETuxaE8eT+dXzxzmbfBTKEmMWjUem+6atxROIGOHTOWaCjRfGs+8DQpqXHnJRecmb3dyQTtx+5KEFwxHIJeHAIYBBOpKvxvPhLXhjF552Idy+GDXD3JimVKuEyAljMm8NTcgkXelSJibvt5SKkLgb7pBlCW5et6Gd0vRpaobTv9dckp4vFR0OKoMSRAv3yXYl/F8HvyLLgXTIBKCInZHTUDdradhpueyH44VqbsB8z1L00ItphjlmprUgUupwBVCtyMhKSvf5HQgmHd/kH/vjmY6Yn0OAAGlTwFqQrqlOhpr3O3wZ332390gJUWNzAlcvSNpVDunYSemfnWZAHUKclOC0TDzhystLROxOgaAKRrMJb3bjw75NU3TpqrQ9zumG4BqH+zAXGnF3xaEHlSkFIUcN2U5CnMXcgLxO0n14lj+M7XKL06YdNuvDLHhLoB6LcpUSitUGq26anoySnhckW5iKFy+o2zdXME0iETgCfzxEMoe881TmlF3i4+pYqp14FI2clyZjromFUJSqkroIa3hmd9Zc+he7v3Rr8cFo8diY7Y6Gja9uk5bBMZo3zRvMg3w/CX+BiClSHq3v8Tt33kyqVZHmpmErJJq91t1z6oWy+Bt+Kly97pMikYSuV4VKiZAH2MEQSaQK3yMzNJZeQZjw92MCienURmqYLar8iitvg2aw+9DjF7PWDMf8xxZGopP3xEEOQpwAX/UkDr5YhSc/XqoaaK0+m24c4+O3wAAwMK55u2f3LtekAMlvdv+4517W3zgQk399llKA1HHniyhZ94GhfOk/jIYARDVWG29f1+n8IfnAEo0kMmbaw4N4KKwjvc73Ar+Yg3gbNmz86k1UW31mUNTS3IusqdePUO5yB4h6sp35Y02r32tjuY/51zT/gnaNyHIkTKFD3xUOc/4Sp5rccY+8Hmma9+JfeD9jkNTzefV598PLW09AOCT1nS5++72jzXnqXhxVSWJ4/+KUEqWudmc9ODIkYJdP8hJJbS+ZcM1c1ofeEluAECJ1EZ5qDwnVt6wnCIf+LpMhGRp8PjJPzcCaKxIVSH2N38fwMsgtXfgmpr7/t3+/3V9Pw+DTALTIMLBBfwcqTUl2Ml0uDPfW3Nc8lFU48Q8q51YDL95NVhf8z7g+Td1rs48q70VDXwz5mfq77wXf1Q4EgDg3WX7s270GU0JdrjIKB/4jV+FgvB7YyDGtaElXsLeuEo8TlbtXkuRwO8AHV++lImekT/p9pMiexvcy12pjkrpO2sO/dZfltjNTwXql04EhUyREmo3+/c/njxM9FYCQMoHPtchOWVn6KXLALtR+kqWgKakPjLstPX1AM6pvY0WzxzhjoK0tFxzXfo0rBHgi/Lach2qQvwo/6LR6/d5O+dfme4ImtdwK/GEEc1fsW4IOTHwakrXNJPemcyzzLa7UFrDk0uYoYZnV68I8dP++PiDeZYpDDmqrip7cu4DX0u9ldQi5TmyaSvVoduf9W9n7gBAjE9x9T4fZst7uOz/KoE98POGOslOKM103CAWeeKSI0BUu8fbTXu2QSBwN3MdS6zzqRsAnL/BR/buqucAAODsezM8U0Bpw+71+/2erQIEruOCwW9rEODL6kIUvVWyso7b2MWlqSb96mj1nREEOQn4nWQnJ3dqy67MMx2SPL3tqxumLicTIKuQ+mCdv955A+/f3WiJUqaekWj2/DJ/wZdQHZLOfg0A3jkrHlZaunIeKuqRkOMiWroXvkSQEwM/EX8D5xrFij6ZhMl7ynnCrAiBcssgsTyHv+kGYdtl1VVF+B0tLkYKYZ0uBSAfCiOQ4S4AwBdDVh1K8/wZAJy/gQ3xJwZMm1898AT+OBiOGEgSRH7Fq9HqVVL4yVautnz0+hUAwMsHjpnyfr8w0wOoNv9EibfLRzsK0tIy2Yerc4LqFv3q6CGDUpyTQSFTpJjxlqZtZd8kRq+rK62eumLGFZLn1N6GDp6lbY0BYKetabWb/fsft/rt3+ntcNjCYrvfWuR3HjAA+Pe4RP1cnL7iEnpJ79pDzUkU8l8ksJdXqwf26J9eAcBv1jVtPfn2P+y0lNqTx3tA1KtxP8BVlTylwge+LCT/y2+AqBfiG6p2v4/H7wiCTAH3ge915U0tWUeu3jV2/wVu8RfPnwG8Hjyy3AeRD3w6YQK5G5VsKuUhSe+2B1FoYbHtRQvliquyhD7w3x1Y7Z3E+yN4BXAuiDKheteAdZf7wJeHpNSdfzUGOP/nH+LyHUHmBi7g501W6Yn5HdNxTF7qyS048pIjAJkJK+8iX7x6Zod3kQMh6ocf7K4/yF43FdmDs7gBVCrwZJ8OSe/25E5YmU9Ue/XWcC1ar1dHkSPmy8WfF26+YlXV28ylywDjcmU65q3RV0kfeHfNu1XoA78T9rAzzzLblsdf1JoS7ATr2yuxDzz9lsg8yRL6cB4c+8C7CVu5CCldsRne8dt/sRj6wF/7UcJ/2e9odDd8URhiw12AxrUfrUC4w0Anu9eVgo08cwLbf5ATSeQDH6nXxURV5JcuA9Cdf/5Oyge+vSMlEua6FbZQThlinmW2O37CB77sqiyRDzxEPZYR5DuJqnvmWdOFwn+LTXcPVPuz2zOv3zF/Hi6YNr+SYAn9USMpLVuF/fp6AADAmeYn3dDaqdttffCnc3i8gyAprfD5ui1F4vJV8d5AZRRBkBNApuVHiz0tR8Mgti6S9G7kXJSgwAeeJOo5sz7wBd063Ac+Lvmc1gUp7wOfJxiOqkLi62/IYT+TsetUmuYhCIIAwAF94DMJc9OvDgkf+JXYyiiqp6+6amrqMoHg8ZM4N4+GgciRFaEYvpeB4iEIMl/wBH6OjAcMIMioOhPVuPI6fFGXCUCj9Mwr1pZflBuQ0SZJqTslVehzfiZTKkHvhxeQVy0F5nubG254yK5eoKkftInRtQ034JPr//l3PlOwJ36uoJApUkT5CXxdJuA6Gq1yjxsNAyBGxoXYdeNkNJUPvA4AoaYcf7cx4akPxwceABK7D0KVasO7pcxwV2xFmRfY/oOcBpJ2Fkknj7wPfD5hbvstBcpD9QIf+Mk3nH49LfT41rylLleqS/vAl4RCxC5u3gp5P+Df8iGDafOrCC7gj5qAugGESZwnV8eS7VWdL1i5Sn2QmyTzZnV3zZNXdUXKq8DloNti4smPpg4T5t0f5N+7Y7oJZ7mA0gCgJuSkq6MAfsdMPuOrgWNa+7ZCQYqoXqLjuh2ZlrfCBz4pgM9BH/iTBbb/ICcR9IEPye0hIMcPps2vJLiAPx5qV+LqJ9ehGXW3ot3Lah24lI0cX+xT16R8KFFVQg9pDc/8zppD93Lvj349Lhg9Fr9n1VHmbVAAYtirujLqaA4lFyEI3E1fRzP4A5DJ12i5hByYYh/4KvZb/bNPH3i+2idFu30p4Tve4B75wBeFJGjMqP+BIMjbTugDn1XbrOKk+MAXkWrl9ztJH/iKUEILAEGQ+YIL+KOG1MkXo+Ds15nYqVVa/TbcuUfHbwAAYOFc8/ZP7l0vyH6S3m3/8c69LT5woaZ++yylgahjT5bQM2+DwnlSfxmMAIhqrLbev69T+MNzACUayOTNNYcGcFG4f/odbgV/sVZhnCRmy6qtPnNoaknORfbUq2coF9kjRF35rrzR5qVbE6JCil9XJIARAEDjOwasu/v8LUQQ5CA8fwYANNPyAwBF6+OkDzy8BmA898zqAx+r0ANM7wMfFReJI6eoJIBXNu38VHPDUqFmE4AIH/iy0Ph3C0WHVy8mPDty1Exs/8HOIOTIEcXlmw9hdz1RHUmM9nL5RSfCBx7g91/kWyITLUxEvRqAaitThODhf6MQ/ozMqUYLSTB9rsOuya8euICfI7WmBDuZDndeIk8+imaozLPaicXwm1eD9bVQNDR1rs48q70VDXwz5mfq77wXf1Q4EgDg3WX7s270Gc1IZJQP/MavQkH4vTEQ49rQEi9hb1y1eyqrdq+lSOB3gI4vX8pEz8ifdPs/ir+qtyF2pKujcdcrhHu7zLNmV7hD/VJA0VFkRmo3+/c/LoykJ4J82225DgDPfxuNyXRICh/4Ag35DLEKfV0mQKPqm0ofeM9xIy8mCfyO5kQlAZJ+peZuDcIv87d3tK1x7aYuQVVIaneNaCIMAA//q7b+m/M3foBT0BNJ4QQUy46QY0NptRf/R3vdXVhs99vcO5N1Vsy2C1AvKSnPJExh284TZkWI12OG8hzcB35VmXTDMvxfPngJUEu+80PNGcBFo9fXJWCdFZPC+3eVSSEAv6Ot/yZMyX5Hm6ZGC5mR2XIdJsOvEriAnzcFJ1rE6EUHVN6aGwBRbWEFz9f3BdXjrPOpGwCcv2Hfu61IwB52Pl2nrwDOvjfDMwWUqnavq0jM75gOdZ2AiHzsWaYblB59K3qr4F2AsAEsatGHqEmfTI7mENP+Fcz7CHLEVPrAG1bCBz46l7/0TYDxTlvTVLvfUlr9nhwPE4llejI+8MTo2Q3TKfKBf/KIKx+nDs1pt3NroyUx7/MxNJvxl2k2YfC5xz7Wq0IgcVM8TQtvVzfuze6EhCDIW4Tfae+8IcbdhtuOUwhcVclTWn4akUmYyVPripCkd3sQhVIlUhVXFT6zViBDN3wNsAB7YWZVb6p065HHrutSRUjUfp6Hl+JBUrsMCIIcNriAPw6GIwaSBOExliFW7wAgKbw0NScaOnr9CgDg5QPHTHm/X5jpAVSbf6LE2+Uj5WVpaZlUa+OVUd2iXx1NwUJB6Zn3bVFUEwBFR5F9M8EHXmzERemhIUtpqfgpIHI9njEWdq4DgNKyVSrGBK65YfT6rfwwaelD4rrJAicuFiVDlFd/0L0nJWOPnzB9qTykhz1NvAbIdMGYXDtQBtYBHT1YdoQcI/42t9C8LunXkycdzLMA6ixSt2vAE0tLFg8lqWg3ms6jbj9XMc9yKDF6q7Bmugkf+NEwSC/8mbe7xQvzK0L8i7z7Ue+zQ1iwY/6sBnMdAriAnz/ZCTHzO6bjmLyyiJsS59suATInTryLfPHqmZ2novWIqB9+sLv+IHvdVGRzesKsXW7MKOQk6d2e3Akr84lqr94arkXr9epoCFfIC4AYPSy7OhgoOoocMsyznMhDjnmW6Vhyr8vPrOPaer+jOTQ8d2eeZZoWxLPUwDW3bS6A5Hc0x+zI+e0CUaovDu6ZZ5mFwyDVegMQTjtlSYg8pQ690omtIiSeYXO60n8EQRCACvE2Se+mKhep+9jo9cMGR7FVWJQwpwxlUmTFVQVP1tcBgHn52Aw7CaEKgKgf9TddlKNHkDmCC/ijRhLnS/sz5gQAgDPNT7rtZBe5dcjPdkAkpdWNxUnB3w6SP2jV0egsr+xYDkGQ+VPQ8iMW7aNhoq8lt3rmCAvg0EpdSN1FDZlJgw3lmgo0XxrPvI1Uz+a0NsZc0E7cvkT6PhiOQC4PxWZxfC5+7SCbiFjncgxg2RFyXEwp1wkAoK5E6Y15a24Q91SKhLnp6y2lIpTeYOTrZlGqXnXV1OSU8PhJ03BUGZKi8n3RQLDv/qkE+Ac7Acx1CC7g58p4wACCzISYqMaV1+GLukwAGqVFq7G2/KLcgIy4aEqeOalCnzMk3beV02ReQF61FJjvbW644SG7eoGmftAqo/6PNecpAAAErmkNjdUWLuIPCoqOIvunvIS+LhNwHY1WFdnnLID5+jtORhOVKeOdgWR1fmPCU4d9N4ey9yd2IfJWnsjJBsuOkJNDurs8cTCRXOWXJMxtv6VAeag+3E1tMCqtfn/yDafPZxklPL8Tf5GKUKL8qhtWCWguKtHPBcx1COAC/ugJKO9+kusQJlfHkm2h7iZU7IJc1uPN6pEOXKgCVw7dFuIhkdfSocG8+4P8e3dMN+EsF1AaANSEH1R1FB5aYvUeRl2TDjHvHwRctyOHjKR3+0vc/p3nkzlNzRITX9Xut+qeVa2cwRf6qcqdulwoj0nkelUoJDcHRhAEqSR3bhKZpbPyDMaGu1BkXdmoDFUw21V5lFbfBs3h9yFGr2esmY95jiwNZcqvQj1SVLFDkDmBC/jjoXYlWf1EM+puRac/1TpwKRs5vtinrkmFErSqEnpIa3jepr6Xe3/063HB6LH4PauMss7/XbAVQdGBZDbQSAmZG9H5OOMr+cl/ovut/vE7Dk310bAJw7XM+IhUeT4b7iaO/ytCFb2syMkHy46QY6Iuk0xmmYwkNwCgpGuwPFSeEytvuD+iDQgAAL8TQGNFqgz5wyCbOOsySReDIocH5joEF/BHDamTL0bB2a+HoqRKq9+GO/fo+A0AACyca97+yb3rBflO0rvtP965t8UHLtTUb5+lNBB17MkSem7mQeovgxEAUY3V1vv3dQp/eA6gRAOZvLnm0AAuCvdPv8Ot4C/WAM6WPbuYLau2+syhqSU5F9lTr56hXGSPEHXlu/JGm5duVUbrT3ZfFX/c6+K3EQSZA8+fQWEPfNH6WPRW8hevARjPPbkOySk7Q58/A3jJbwEAZDk+/k51CmWIiovEkVNUEsArm3Z+qrlhqVCzCdy1viLE7/k3P6PinwHlOE4K+52k4lwWOXJCCbeHsLueqI4kRnu5/KJcwgw1PLt6RYif9sdl8cyzTGHIUXVV+WP8/ot8S2SihYmoVwNQbaU6VJcJwL/+w19pzkh8897KMEjkVmQOzCPX4abAaQEX8HOk1pRgJ9PhzkvkyUfRDJV5VjuxGH7zarC+5n3As23qXJ15VnsrGvhmzM/U33kv/qhwJADAu8v2Z93oM5oS7HCXED7wG78KBeH3xkCMa0NLvIS9cdXpk6zavZYigd8BOr58KRM9I3/S7SdF9jbEjnR1NCuHxc3pidH70WzH72+zAQmaiyAHo3azf//jwkh6Isid2pbrAPD8t9GYTIck89amU3O/dBlgt1kDMaONRJdYRacQ85ykF5Pf0ZyoJEDSr9TcrUH4Zf72jrY1rt3UJagM8R3Kc+KWo45matjJc7xgPRFyalBa7cX/0V53Fxbb/fYiAACwzorZdgHqJS05mYTJC5BEwqwIpS3W/U03IMaqMumGZfi/fPASoJZ854eaM4CLQuW+s2JSeP+uMiHEM+vOnvj2/o810wH08jhVYL49XeACft4UnGjFGqFigkpUW1jB8/V9gWgo63zqBgDnb9j3bisSsIedT9fpK4Cz783wTAGlqt3rKhJfK7tOQGJDEjcoLQNT9FbBuwBhA1jUog9Rkz6ZHE18yXBjlxiHI0eFIMi+GG9p2lb2TWL0urrS6hmWGXdXRgfUl74JMN5pa5pq91tChzgctm8hYtE4GWZNYvTshukUpCT25BE/6El1fNJu59ZGS2Le52NoNuMv02zC4HOPfaxXhMDboFBffHe0AwChHT12cCIIMg1+p73zhhh3G25b6LADAFxVyVNaLt6ZSZhJZZGKkKR3exCFUrVCFVcVPrOWl0hiw9cAC7AXZlb1pkq3Hnnsui5VhbzPx1BfvLgTfftzABdmMq9HEGQKcAF/HAxHDCQJwmMsQ6zeAUBSeGlqTjR09PoVAMDLB46Z8n6/MNMDqDb/RIm3y0fCI9LSMqnWxiujukW/OhoxGgaEkCAIAtcp9S6dyFvtooHmIsisXLoMMK6a7klyAyBKDw1ZSkvFTwGR6/GMsaxEXRhtAgBA4JobRq/fKnBXXvqQuG6ywImLRckQ5dUfdO9JydjjJ0xfKg/JwwBA/Yuf/awtIvly1Ol5m+uADgusJ0JOEf4213C7LunXkycdzLMA6ixqBG/AE0tLFg8lmcF+vZqqq5hnOZQYvVVYM91GXFE5GgbphT/zdrd4JpwYSuTPKLNi/jwVYL49deACft5kJ8TM75iOY/JST+6hmdcMBcicOPEu8sWrZ3aeitYjon74we76g+x1U5HN6fH2sCQ3AHZnuaekd3tyJ6zMJ6q9emu4Fq3Xq6MhSquvAIh/o6R7NDIlaC6CzAvmWU7kIcc8y3QsudfVu305WVsfOQkJBwzTTG7FBa65bXMBJL+jOWZHzm8XiFJ9cXDPPMssHJa3oh9FIkoMIONYl05s5aHZJskIgrzllIpfSno3VblI3ceh0xqE246FCXPKUCZFVlxV8GR9HQCYl4/NtpOA+RNBjg5cwB81kjhf2p8xJwAAnGl+0m0nu8itQ362AyIprW4sTgr+dkqUtDqaudEtg9BZ924RBJmVgpYfsWgfDQNQV+ISzvTqmZNxEhJSd4mtuNhgQ7mmAs2XxjNvI9WzOa2NMRe0E7cvkb4PhiOQy0NL2QN3vr86m4gyFrwcAlhPhJwWppTrBABQV6L0xrw1N4h7KkXC3PT1llIRAn8zoS3C295Fr0/VVVOTKz2KM+FsIcyfpwLMt6cNXMDPkfGAAQSZCTFRjSuRvHpdJgCN0qLVWFt+UW5AppozJc+cVKHPGZLu28ppMi8gr1oKzPc2N9zwkF29QFM/aKVRXoUbVyr4Hc2h71ep4SMVoLkIcgCM0//JAAAgAElEQVTKS+jrMgHX0WhVkX3OQp2vv+NkVN4LGl4Q7Qwkq/MbE57a75huAOrBlDMy+k9+J98cihwlWE+EnF7S3eWJavnkKr8kYW77LQXKQ/XhLoB6LcrCSqvfn3zD6VfwFZlwthByGsB8e+rABfxRE1De/STXIUyujiXbQt1NqNgFuUkyb1aPdOBCFbhy6LZQX4q8lg4N5t0f5N+7Y7oJZ7mA0gCgJvygKqO88Z46nWu9liKJtP+bcfpnCNkfhTkXV/XI7Eh6t7/E7d95PqlWR5qZxMRXtfutumdVK2fwhX6qnbQuk6KRRK5XhQCUVt8GzeEtTcTo9Yw18zEWhSIIMoHcuUlkls7KMxgb7mZUODmNylAFs12VpyITzhZCEOTQwQX88VC7kqx+ohl1t7jONKZaBy5lI8cX+9Q1qVCCVlVCD2kNz/zOmkP3cu+Pfj0uGD0Wv2fVUUlfUR87lDpm4iFrUR0ucnDQHQSZyBQ+8NH5OOMreS7lEfvA55mu+if2gfc7Dk2JO7HKCx9a2noAwCetac+33e0fa85T8eKqShLH/6Jyn3mWOVzpybuJEE+e4o4/bQI0VjANHR9YT4ScEuoyAdgdMjZcM6f1gZfkBgCUiHmWh8pzYuUNC0iUOQVO51oyf1ZkwtlCyMkH8+3p4j8d9wO8dZA6OQ9w9uthDlZa/bZaWwjDC+ead4uzr6R32zejgQs1VSXA69hBlNCzwRhAdJGeJ3zfk6hGr/vnv2PhyHAg8zuWpmna/Wf8fuJl9LoQv6OZDgXVVmuZCBfZU6+Gp1uEqHbbIEC3/YlRAFBa3Z6hhju15wDg6vdw+Y4gR07tZj9HQT6S9FUj/HNO+MDXZQLB4yfxBHPKztBLlwGkZphVyHJce8M7hYrZaevrAZxTe/1+v2+rQB2t4wMASEvLNQjoU9XmX8BWn9IAanKd1/tAMByFZfei6FQ8od/RHBpd1RwMoPa/oQ38sfPl4s/5f8k3v7bzff7fcT0VgiSQ9BUVAtfkCnKCngFu2y1fcOcSJvgdTbM8Vhnip/3h7AmAeZYmUl/FVTmi2qWeQQBqcf6Eykw4Wwg58UQZtTDfIicQPIGfI7WmBDuZDndeIk8+imaozLPaND6ffvNqsL7mfcCnzKlzdeZZ7a1o4Jsx3+Z85734o6AZr6vfXbY/60af0ZRgh7uE8IHf+FUoCL83BmJcG1riJeyNS6XlAEBW7V5LkcDvAB1fvpSJnpE/6faTInsb4qyrOirUVmQY8ZpZ6GgOfafkCaYADUgyoDsIcgj4SbF54dS2nFmZZ9ogmbeWEFuairpMgEaiS6yiU4h1/nrnDbx/d4N7zCmtnrEbG3B+mb/gy8QjRnUGL/7OGYRP+NByKNRu8nzNvI0BnIMxd4+f+vmR+YP1RMgpgTtvvoFzjeJ+wEzC5I3jIh1VhHg9ptCtA3/TDYixqky6YQahhNfVJeYBwOW7xtl1kT8rMuFsoUP/h0UOE8yopxRcwM+bgpLUWCNUTFCJagsreL6+LxANZZ1P3QDg/A373m1FAvaw8+k6fQVw9r0ZnimgVLV7XUVifsd0qOsEJDYkcYO8NLRA0VsF7wKEW8JRiz5ETfpkchQA+O8MMXotBcAv/AQEQebPeEvTtrJvEqPX1ZVWT10x4+7Kc2pvQwfP0rbGALDT1rTazf79j1v99u/0djhsYbHdby3yOw8YAPx73OJ+DuCCuFdcQi/pXXuoOYmseZHAXkFKYo/+6RUA/GZd09aTb//DTkupPXm8B0S9Giffqyp5GoqA1mUS29kPBlErP/sDACzE/wCq3VvemNnIGEGQtwnuA9/ryptasgddvWvs/gvc4i+ePwN4PXhkuQ8iH/h0wgRyNzrxUcpDkt5tD6LQwmLbi1JUxVUpWEYJj8NPXMoz4dJMIcyfCDIHcAF/HAxHDCQJwmMsQ6zeAUBSuO1HTjR09PoVAMDLB46Z8n6/ALOg2vwTJd6xFPk+cT25We5Y3aJfHY2W74eT5tHxIgu6gyBTcOkywLhcmY55a/RV0gfeXfNuFfrA74Q97MyzzLbl8Re1pgQ7wfr2SuwDT78lin0uXQbYFQVE4Tw49oF3i06QpNuf9W+n3vE7mkO//ReLoQ/8tR8l/Jf9jkZ3xZWR+7Flug07/XW/nfr+/nbCPX5fYB3QnMB6IuREEvnAR+p1MdfD/3HpMgDd+efvpHzg2ztSImGuW2EF5pQh5llmu+MnfODLrkqRcALl/9PvaKL8kgFUZsLZQvsC8+eRgRn19II98PMmbAiKmqJsNaCOyXuNRsOAyy8lMN2A74Mm4V3ki1EXORCi3r1RqKc8Bdme1NjbiWugzIKkd3u2GnW5qzZvrJoiyjzLifcQkEOnrBQKS6SQHNTRsoi+yNEwiA9sJL1b1Btf4ANPAnct7sBM+8Dn8lzoAx8bJUtLyyTZ7llG3gc+TzAcVd1itqsQBEEO5AOfSZibfnVIVL+vxErIQDd4kq26qvLxp8yfs4UQBJkDeAJ/1EhKy1apQ/dnzAkAAGean3TbyS5y65Cf7YBISqub2Hr2t4NkP31ZlHmPAwDIOJ9QR6NkejFVBEEOg7fYB/5QwdqWeYH1RMhp4DT5wGP+fJvBjHpqwQX8HBkPGECQ6YEnqnHldfiiLhOARumEmHeNDsagL8oNAJrqJuLyzI3sSClvSDqlldN+eAFc1j713Mz3NjdcroZHVPUCTf1WlUZ//0XJh5RaUyH7Bd1BkIPy1fCBr2C2q5Cj5cvFnxcqz2NOQ46TU+sDv7/8OVsIOcFgRj294AL+qAmoG0CY1fjuqGPJtlB3Eyp2QW5uzJvVIx24UAWuHLotJEp5bdRhwrz7g/x7d0w3YfYeUBoA1IQfVGX0w4/Ig8LvIiVU9b/iHM3SGjMycjCKfeCr2O/m4T594Plqv7BWJyV8x4a7Uxz/z3wVgiBvN6EPfLEAcDHH7gM/W/6cLYQgyOGCC/jjoXYlbl9yHZpRd4sbRWOqdeBSNnJ8sU9dk/KhRFUJPaQ1PPM7aw7dy70/+vW4YPRYbEhXRhNiKgAQncC9JV3xaOCBnBCeP4NC14yi+Z3ExTb5i9dRtUxdJulCoSk7Q2MVeoAiH/hG4VXR3qQ4coq2PfnG6M5PNTfcaWw2IW969/svUnVE6XJ/v6M5/4sUXIUcN1hPhJw8hIHb5kPYXU8cSBCjvVx+US5hxm6dFaHQBz4sixdynP2WUnXD3Gxqlvw5McTzJ/Msc7hiQ4HVKHLywIx6SkERu6OG1Ml5gLNfDzdRlVa/rdYWwvDCuebd4u1TSe+2b0YDF2qqSoDPP0GU0LPBGEDIQJ0nPG0S1eh1//x3LBwZDmR+x9I0Tbv/jN9PvIxeF+J3NNOhoNpq9mici+ypkcgeIardNkL1qepolufPAOCc+lYs3xHkZFG72c8R6ctrViRHxw005DoAPP9tdLWkr6gQq9YJH/iViX/Kly6HBTd1mUAsulTpA+85rvBi6vf7fVsF6oSCe5J+pQaDQfhlbtYGg2jTNMT/5YOX6TtyNSin44tCANib6uGRYyGaZRbWfyLIUaO02osLdN1li+1Is1h94bbdAOrL5T7wiYQpbNtXdKkyBMotg0S6dcIH/pYy4YYZZsyflSGRP3lL/Yu/K/to5Ej42s73+X9Tjv9y8ef8v7k+FXKI4An8HKk1JdjJdLjzEnnyUZTOmWe1aXw+/ebVYH0tdP1Inaszz2pvRQPfjPmZ+jvvxR8FiZLzd5ftz7rRZzQl2Glcigd+41drDu9G3xsDMa4NLfES9sZJ4bkssmr3WooEfgfo+PKlTPSM/Em3nxTZ2whtRSuj6Q/zO1tjIMZPWjOn/VNkQIIGHsgJo9IH3rASPvDRufylbwKMd9qaptr9ltLq9+R4GDF6/X3N4DI+8MTo2Q3TKfKBf/KIqxunOj5pt3NroyUx7/MxNJvxl2k2YfC5xz4Wz5IWmIpRWn0btKgI4Zzam0eXP3JgsHAJOXH4nfbOG2LcbbhtTYvevaqSp7S8jjyTMJPKIhUhSe/2IAqlSqQqrkoya/6sTK1Kq3/zmebwqctgMCeVFGQSmB7fEnABP28KSlITjuf8hImotrCC5+t7d9PXM4mPdT51A4DzN+x7txUJ2MPOp+v0FcDZ92Z4poBS1e51FYn5HdOhrhOIeTbXMynt41L0VsG7AKGCS9SiD1GTPpkcTX1Nb4MC7toiyNEzwQde/B1HB+INWUpLxU8Bkevx2rnMZUL4dAAAQOCaG0avX7CdJy19SFw3uT/KxaJkCMsDjB9070nJmKgsFb6VvVVYM91GdhtSafWMXfPxstFw3V156m+GIMhbjb/NLTSvS/r15ESJeRZAnUXqdg14Ymnh4Xc2A1a0G82mBld21Yz5c2lSat0aR6nVxtU7gswTXMAfB8MRA0mCKFGK1TsASArvLc25foxevwIAePnAMR8k378w0wOoNv9EibfLR+3m0tIy2cd0PEF1i351NMbfdINCBYD9cJqsL9DAAzktMM9yIg855lmmY8m9rt7ty8kmS7+jOTQ8d2eeZZoWxLPUwDW3bS7O7Hc0x+zI+e0C0bMpDu6ZZ5mFw3LaGVwvT5UlIfKUOvSS5AbAbvpC5hV/zTUXjJ4ub1bnqgmcojqgUwcWLiEnD3+bFlcvSno3dfBB3cdGr9+NznAs04XChDllKJMiK67KPtZs+XPm1LofMH/ODKbHtwfsgZ83qp3uJ+3ZakAdk/cajYYBL2FKYLoBry1PwrvIF6MuciBEvXuj0LVjCrKbsnE+5iKmsyDp3Z6tRl3uqt3rGWTKaAj/CXyLjt/Lipqw2Ak5JqijZRF9kaNhEHsPS3o37I1PwrwNmpCflPRVgyQaMpP6nMo1NZ/nxB0SOUBaWi4Ty8hcZzk0vP1oWLgLGQxHk2+z5sLbIZ+JIMihMaVcJ0BqisO8NTfIJcxNvzrETzrC2/DWc9EQX3VV5eNPmT9nT60IghwyeAJ/1EiiQDR3xj6ZM81Puu1kF7l1yM92QCSl1e3He83+dpDcka6O8rcoxEsEBEGOnPIS+rpMwHU0WlVkPxoGQIyEXlNa2X0KT6H4XChZnd+Y8NRcNUm1p3VPKiE8fT+E5TtW0MwRLFxCTjxpnY1EtXxylV+SMLf9lgLlofpwNzVTUlr9/uQbVkysDil/HiL4hzw7mB7fGnABP0fGAwYQZHrgiWpceR2+qMsEoFE6IR4PGAAMxqAvyg3IuIOk/JXikZLoOI/nzPv3Yp7MC0gZMIkn8r3NDZer4RFVvUBTv1XVUWDeD7sUuGZArAnw1QcNPJDTgaR3+0vc/p1ntDlJFCUmvqrdb9U9q7rxhi/0U+2kdbmwOGnC+dghLt+RefLl4s8LpZUxeSLHRm7apbR4r5CoaS+EDXczKnKcRmWoghmu2l/+nC21IkcIpse3B1zAHzUB5fIlch3C3VHHkm2h7iZU7ILc3Jg3q0c6cKEKXDl027uliC6oQsHl2WHe/UH+vTummzB7DygNAGrCPKU6mpPCCqhjFjuXflXJ51Zc0iMnj+h8nPGVPExcw+9389DvODQl7pStsc8O1zLjI1JanGy4O+n4nz15HEAQpOa+rqk9fqsSEYIgM1GXSaGvThW8ZbEkw5SHynNi5Q0LmC1/7je1IggyD3ABfzyEnsSSvqK6Ds2ouxXJuFXrwKVs5Phin7omFVZOqkroIa3hmd9Zc+he7v3Rr8cFo8diQ7o6yju6oCY0sOt8DyNVbvA2gRYgyLHw/BkUumYUze8kLrbJX7wGYLz6py6TdKHQlJ2hz58BvOS3AACSME1OFRpliPYmxZFTtO3JN0Z3fqq54U5jswlAljOP8fsvknVEkt7t6zl/ubdJkuMUgYVLyAmDz+XczYewu544jSBGe7n8olzCDDU8u3pFiJ/2x2XxzLNMIShfdVX2w2fJn5NSa+IsJnA619BG7lg4SHrEpHqKQBG7o4bUyXmAs18PN1GVVr+t1hbC8MK55t3i7VNJ77ZvRgMXaqpKgM8/QZTQs8EYQMhAnSc8oxLV6HX//HcsHBkOZH7H0jRNu/+M30+8jF4X4nc006Gg2motE+Eie2okskeIareNUH2qOsqpL54RM31J0VdUlEVBkGOgdrOfI9KX16xIjo4baMh1AHj+2+hqSV9RIVat4y6ZU6yBL10GkJo14DPaWHSJVRQaMc8JvZj6/X7fVoE6oeCepF+pwWAQfpmbtcEg2jQN8X/54GX2ntwJKrwfEAN94E8mX9v5fnKjM/MSQY4BpdVeXKDrLltsR5rF6gu37QZQT+xJJskkTF6AxBNmRQiUWwaJdOvA33SD8NCn6qo0M+bPqtQaVeP3DAJQS9wQOQa+XPw5/2/K8ZhUTx14Aj9Hak0JdjId7rxEnnwUpXPmWW0an0+/eTVYX/M+4FPm1Lk686z2VjTwzZifqb/zXvxR4UgAgHeX7c+60Wc0Jdjhhsd84Dd+tebwbvS9MRDj2tASL2FvXGyFwpFVu9dSJPA7QMeXsw7KZ+RPuv2kyN6GKLaqjopzu53/wu8dmsHP3FV1qg1I0AIEOVbGW5q2lX2TGL2urrR6hmXGFebRufylbwKMd9qaptr9ltLq9+R4GIn84KZE0rv2UHPCOgBi9OyG6RRUprInj/gOX6rjk3Y7tzZaEvM+H0OzGX+ZZhMGn3vsY/EsuYN2cU+sBkUQZDb8TnvnDTHuNty2pkXvXlXJU1qeVDIJM6ksUhGS9G4PolCqRKriqiSz5s+KENfGt7u6xDwAuHzXOLs+ST4PQZCZwQX8vCkoSSWxUBI/YYpF2/j63t309UzOZZ1P3QDg/A373m1FAvaw8+k6fQVw9r0ZnimgVLV7XUVifsd0qOsEJHYUdYPSPi5FbxW8CxAquEQt+hA16ZPJUQBJX7WHa45jRv9S5CQJoiLI28Cly6KJpWy+JckNgOhAvCFLOfWKSRC5Hq+dCzsvAUD4dAAAQOCaG0av3ypwV176kLhucn+Ui0XJEJYHGD/o3pOSMVFZyjzLocTorcKa6TaS25CjYQDqiiKeIlJ2noVTvY14wsFdTuQEwst3VvXrkn49OVFingVQZ5G6XQOeWFp4+J3NgBXnFrMdaZRdNWP+XKoIxdr4XCuFeda+VQFCMH8eMZhUTyO4gD8OhiMGkgRRoowl1yWF95bmti1Hr18BALx84JgPku9fmOkBVJt/osTb5SPbUGlpmexjOp6gukW/OgrARtu7qY8t30SYzOm2ykALEORkwjzLiTzkmGeZjiX3unq3LyebLP2O5tDw3J15lmlaEM9SA9fctrk4s9/RHLMj57cLRM9mP67KLByWMJwTjIahMyUDyJykS3IDYDd9IfOy33C4CwQ2NE3MV8p2GBAEQdJwF9yCk3ZJ76YOPqj72Oj1u9EZjmW6UJgwpwxlUmTFVdnHmi1/loUqboggyBzABfy8yZ5oMb9jOo7J5ZtHwwAA8qYfAJn1K+8iX7x6ZucpX+gSon74we76g+x1U5HdlI3zcTpP7wdJ7/bkTliZT1R79dZwLVqvV0aZt+bQgBh2dD7fWXPc4ln7Vx20AEGOlYKKIZHCksfT+ckah3kb/BRKEqNWjcemu+YtRR7IkT6nck0Fmt+nY94GBdWO5ptT2RhDKMgkbl8ifR8kvTULGA0DCNSVfjeeCWvDGb3ycLttjuAuJ3LSmFKuEyAljMm8NTeISzJFwtz09ZZSEQJRrJ5UQt7grkNVV1U+/pT5U542taZuuH/wD/mowaR6CsEF/FEjiQLRGVqDzjQ/6baTXeTWIT/bAZGUVrcf7zX726n919JoWIagx2UIrZUZ/4kQBDkI5SX0dZmA62i0qsh+NAyAGAm9Jr7+jmd3ExvM452BZHV+Y8JT+x0zbL+cMLKKyLk5fJTExBg5OeAuJ3LySetsJKp5kqv8koS57bcUKA/V42J1AEi1+1TcsGIudTj5c543ROYMJtXTCC7g58h4wACCzIkWUY0rr8MXdZkANEonxOMBA4DBGPRFuQEZd5CUv1I8UhId58kd0f16MU/mBSQNmMIn8r3NDTc8ZFcvpJXoqqP/MXzUsR6EUeO7M3iqflWYk0PSy+99ryx0/he/OJSPQL7KSHq3v8Tt33lGq1rJH4DExFe1+626Z5U03ggi6eN4sliXSdHImZpIJx3bIwiC5KZd0XYgK89gbLibUZHjNCpDFcxw1f7y5xSpteCGCILMAVzAHzUB5fIlch3C3VHHksPqcaFiF+TmxrxZPdKBC1XgyqHb4uQoMvs8NJh3f5B/747pJszeA0oDgJowT6mM8l8++mAUR902QHof+a0DNz6R46DIBz5cqvudVN+l62hQsIb/4u+Thef73Tz0Ow5NNZ8//H8DAPj30uGaQ4HcuAG5MtF//Ye/0hyeVIjRXt5NHP8nDvdfbEe7hCWCfLmNSuT4QR945IRRF4cObLhmTusDL8kNAChZ6paHWO6dqW5YwENLWw8A+KI/3S60u/1jzXkqXlxVSSJ/VoT+9o7GvZKCZOcUchrApHrqwAX88RAaZ/IqTZpRdytqHKrWgUvZyPHFPnVNKqycVJXQQ1rDM7+z5tC93PujX48LRo/FhnR1tIR3UP0EQY6e0pP1RKWm6K3MjqjLBGhyermPztAYkjBNZv+STzZRzLMcCqptg+NQUOOAtNQ459KdN0LJye9obReALNcBEjJ7S08s0/0DNVPbEIkv7/9Yc54C+T8/3tfDI0dHxRQTp6HI0cLncq5pJr0zmWeZbXehdJ5Tl0mmstIPJUErQvzMIy6LZ55lCkH5qquyH77T1tcDOKf2NloS+B3NCXdkpaXlmuvSp2Ey5NukteU6VIUAHv5XbWsMTbt/TwHwO9rsEiLIUYMJ8zTyn477Ad46SJ2cBzj79XCWq7T6bbW2EIYXzjXvFm+fSnq3fTMauFBTVQL8eAhECT0bjAGEDNR5wufMRDV63T//HQtHhgOZ37E0TdPuP+P3Ey+j14X4Hc10KKi2WstEuMieejUsryJEtdsGAbrtTxHdBairN9SoNouoN24QGGz7Fc+CIMhR4nc0zfLCvMWVK7Irc0lfuQpvAH7LT8y5S2ZCs2kydZlA4G6KP33mrdFXJSO5XJN6lTq5CiP2ZPcVAOwN+QG8cq0JAOcaS1JOZg8u31SBbngMeD2UyEkA4HecpwDnb6xen/rZkZPA13a+n2zmzLxEkCOEn4i/EcmnIK6vqBC4a2Fe9TsOFQmzIgTKLYOEaQvA33SD8NCn6qo0rPPXO2/g/bsb3KNTafWMRPr7Mv+sX0J1aKf9N7+Bc2rvnvDhvJW8H3JiwYR5esET+DlSa0qwk+lw5yXy5KMonTPPatP4fPrNq8H6mvcBX8KnztWZZ7W3ooFvxvxM/Z334o8KRwIAvLtsf9aNPqMpwQ43POYDv/GrUBB+bwzEuDa0xEvYG1cZf8iq3WspEvgdoOPLlzLRM/In3X5SZG9D9LFPFf2w1brdiqPWA3QQPVT+pDyE/2JIgnwVPTF6XV1p3X1fW090V56/0ZM3NTHWNTVXtfst5ROj/tT9zbqmrQMA1N5P3G8hKqZnnmX+PwDwIuzWPBc6Ykp61x5qTnTJ4o1m8GDwxf/HsodYXAMEaFjJKT6GGL2uzv2LbqrxNyEXIdh9wkDPWRu9Fx9ZSdwTT9NE6Jza++w2lgEhCDIZ7gPf68qbWrIHXb1r7P4L3OIvnj8DeD14ZLkPIh/4Vr/9O70dXUHuRhNGpTwk6d32IAotLLa9aIlecVUS9uifXgFAlKnDt/9hp6XUnjzeA6JejfPnVZU8pcIHviS0BC/fALyhZurXI/gFioAiyJzABfy8KegpjU0+xBEVUW1hBc/X9wWuH6zzqRsAnL9h37utSMAedj5dp68Azr43wzMFlKp2r6tIzO+YDnWdgCQ6W8s92BW9VfAuQNjHHrXoQ9SkTw4aRRDkSFBafRs0p1xm/r1vAnzJizGZZ5nug5SpOw1lkc+Eheh5R/dkzf1eAKk6zG+JVTX3U+YRcQ356MNKG+NECSkAAIwAgMj/R6v/cSsekGhL5YUD/A4ZMw8uPcW/j9WafeKJm2LHwoV/swvf/9rO91/8Z+eIHwZ5m4h84DNmFgAAURXPpcsAdOefv5PygW/vSKLEnXmWuW6FBzhThphnme2On/CBL7sqiXT7s/7t9BfoaA799l8shj7w136U8K/3OxrdDV8UhjLOoqEd/Yxt8Jg/jwZMmKcaXMAfB8MRA0mC2EGtFTuo8d7SnOvH6PUrAICXDxwz5f1+YaYHUG3+iRJvl48qSqWlZVKtjVdGdYv+QaL7Bl0rC3lZHsJ/MSRBZs+xXGy+2tR9oqN74eXiKvGJ4jx+d5/fINcIOhoGADAcVYbiR+dz8WvYvIkgyFSgD3yUQEMfkcRJFYIghw8u4OdNdvrL/I7pOCYXC+GTx7zpB0DmEJx3kS9ePbPzlK+vCVE//GB3/UH2uqnI/szE5syS3ADYneWekt7tyZ2wMp+o9uqt4Vq0Ij9IFEGQI2Rqe7hqU/eJju5Fl/MtzeVEfqrLZN8ZSehJhTLIfifukq8IRc/A2+TzQqL7ATfFjod/K43g/0eQo+Tt9YEXJQh+RzO1xzOeweNf6xGBCfM0gwv4o0ZSWrZKHTohoxZxpvlJt53sIrcqBh8DktLqJmrH/O1Ut+lBogiCnDr26+geUr0xMBWiHYBvjRKj1zPWzMd84lwR4uTmwMip4cvFnxcqMKG0MjJf0Ac+C6+sTCniIycNTJinGlzAz5HxgAEEmR54ohpXXocv6jIBaJQeeI0HDAAGY9AX5QZk3EG4iFMjO1LK/5Ls24u5FOZ7axtuwA/T/uff+UxJ9cpzjzl+jE6a7wTpvH6QKHJgzv/iFy+/973C0Mvvfe/8L35xxM+DfMXJObqXuxdnmFG9MkOqFdXvBNBYkSaHEr2sCIIgUxL6wO8ndR27DzzfY4bKKpcAACAASURBVCVFF6S+ChvuJn3gy0O5h6HVZsEIgswMLuCPmoC6AYSrU17e5FiyLfTbhIpdkKti5c3qkdJbqPNWDt0W6p+8uekw8Dtm8k6vBo5phZk/WSULABAEAwCoCUPng0QRBDkinj+DIt1NkY6ePwN4yXcJyxkPGMAfngNcgoyje3LHsQyeEZNzvn98FMDL8xM+9PdfcJ/MVLNSnFaIejUA1VamCMHD/0Yh/DeYupkAOTnwsyO0NUaOFtGcs/kQdtcT0xlitJfLLzpWH/hoaiiO7KN0x7Pwzk81dyCGNpvAO5sqQmkpUb+jOf90Hs9hTj6YME8v6AN/PNSuxPojAIHrmBrHdNygsP9SuWWQeKTppJfvtaYEIAkbOeWaCgDU5fc0XVDVQxB0Z94GBSCG3ev3bRUAyEWIHZul988v5K85I/7vQaIIghwltZv9DGIVe+lynGQmk3N0n0oeU9JXVKBOaDjvd2LrzFL8Xz7ISDT6P9Q0031t9Pr9fr+nvqBP4f0/UyaFAPyOtv4bAGL0+v2+rVJH66CP8amBOxjzmeiXiz/n/x33QyFvDUqrvbhA11222A4zZ0994bbdAOolxxHH6QPv8UkkT3Z9W4U43Un6lRoMBuEvwc3aYBDOWctD0tJyZPzOtUVevqyH+sjICefQE2YyGyNzAk/gjxpSJ1+MgrNfD32NlVa/DXfu0fEbAABYONe8/ZN714vqkfRu+4937m3xgQs19dtnKQ3EsVOyhJ55GxTOk/rLYARAVGO19f59ncIfngMo0UAmb645NICLYjrud7gV/MUawNmi5w4F83VF4jZN0PiOAeuuqKZiv3n5Bi6S+l4wAog19sRW8EGiCIKcQjKO7sTo2Q3TmVxjmupRB1V9H+iofHRaKkrAhq8BFmAv7AVVb6p065HHrutSRUhkzvOhYQPv4dxAH+OTT2aaGK3hj+lxkLcSv9PeeUOMuw23rWnRu1dV8pSWy3oorX5Ptsyoaz1R9VMRkvRuD6JQqgS+4qok7MkjnlZTHfO027m10ZKY9/kYms3xlqZtAQBAswmDzz32sV4VkvRuX+5o8Xc/f8PG1Pn2gdn4yMAF/BypNSXYyXS48xJ58lG0H8s8q03jQ6Y3rwbra6FtZ60pwQ6IIy/mWe34NOrNmFIAgHfeiz8KEodj7y7bn3Wjz2hKsNO4FA/8xq/CfvO9MRDj2tASL2FvXNL/mXL55D2kzLOi5qfRMABQV372i/i7+p31B6IB6iDRfYMOomX8SXkI/9EQ4Gfs48uXphtM5Hq8fI7mkLU42YAQ7AQAgMA1N4xeP7ZWL7489xnyNwG+LDn2Z57lUGL0VmHNdBvxY/OMksi8zNvd4oWlFSH+8e9+1PsMZ50IguwPf5sCMVb165J+PekDzzwLoM6iyUwDnlhaePidzXoVFeezFaOXXSUtfUhct5HKhJbpggzRac0PuvekZOzxE6YvlYd4bQDfPni8bDRcd/dPZ3hgBEGmBBfw8ybfUJq0x+SFpUS1hRU8X98X2HayzqduAHD+hn3vtiIBe9j5dJ2+Ajj73gzPFFCq2r2uIjG/YzrUdQJi9Pq6JHpDpxNi4Y+urigAxSaosagLHCCKU2kEOZEErrltczU4v6M5ZkfOHPWI7su+HkldJMeUXe53NIeKJbYQcC7rAAp3FZmXj80wEw67WEX29Tfd2eXocUfsyLjwb3bh+1/b+f6L/+wc8cMgbyul4peS3k2u54G6j41evxtNAS3TBZElmWeZpgVJYaHJoUxirbgq+1jxkQxAuO0pS0IkL1U0kPYXrggB89ZcMHq6vHkgH2DMn6cUzMZHCS7gj4PhiIEkQVyX3grXqZKirxqPzbxt5+j1KwCAlw8cM+X9fmGmB1Bt/okS18YjYaOStLRMpupUZaFx6AnUeEL7yjJelofwHw0Jyew5liu5xVodyjUVaGbDjXkbFNS4hjJnSFx4ubhKfKIow9/d5zfIKTmNhgEADEeVISk6PxJFoCTafEAQBKmg6AyjhERLOvPW3CA+0ZH49G/T11tKRYjvLYa5le87ilafqqsqH9+zHBqm5BLbomA4Ark8BFK8fJcAtUMQZN7gAn7eZKe/zO+YjmOC3W8pYvKYd+0EyBw+s+ELgPri1TM7T4V4smgUn+mZsj8z8YZqZje1kNDwjRi9E7h8RxDkQEytvV7t1x6f8CRtJhrVl/MtzeVEfqrLZFJGKvjoFdV13DVvqatLoaLSxJA4/Cfh8RjzLFNzZ1Sixx2xo+PfSiP4/wXkuEircySq5ZPTr9EwU+QTb3NCeag+3AVQr0VpSWn1+5NvWJHFwvOYad3nSoiX7wcG/3JPK5iNjxBcwB81kugLnZBRizjT/KTb/lH0knnWIT/bFIi5eLZ5q2jhH1dkwUGiCIKcShJTWNXut+q8Hn4y1RsDU5FSwiNGr2esmY/5xLk0xLwN3sQalkPx439UsTvhfLn480KtY5RNQo6OnOk5FwqCqA+oCDbczajIcRqVoQpmuIpP6VIzurpc2LRE5HpV6BCX78ipBbPxUYIL+DkyHjCAINMDT1TjyuvwRV0mAI3SI55YW35RbkDG3jNlq5xUoc/9kpRVRO0b/8ea8xQA+E/E46hzX3wT+o+bP35MeYkAIVcb/xFtNk8RDb+b39EcuGvsooHoIfDye9+riJ7/xS+O7EmQtw6/49DUTh+b9srDEb+IJtD8YQJorEiVIT+/a1iXCcwqpokgyNvDLLo9ktwAgBIBz/JQeSatvGEBfI+1UEI09VXYcDexsVoYYk8eBxAEqb0D19QeT/8wCILsC1zAHzUB5fqjch3C8ibHkm1uzxaq2AW5uk3erO6uefKqrkjA/Em2ynRbHBzx5qZDwP+hWL1H38Qxd8PszHcNaDQgCJ4GADVRyzVN1F3z5O/ChkMBav/dHc+sHoUgyIw8fwZFupsiHT1/BvCS7xKWMx4wgD88B7gEACRhf5zacSyDZ8TkkvkfHwXw8vyED/39FwDCUTMiUbhP1KsBqLZSHarLBOBf/+GvNIf7KxGjtzLMFPQjJxJ+vBOd/Ew87Zl+JIJMRyiB+RB21xNzM2K0l8svyklyhMqfXb0ixGdMcREn8yxTCMpXXZX98GhqKI7so1knz8I7P9XcgRjabAJPhOUhSen29ZyrZ6EFPXKyOWB63G82RmYGF/DHQ+1KUn+Euk6qxipWd4rhpsRBZmR0v6SNHF/sU9ekfChRVUIPuoZnf/OzQf7dwDV/KPfvKQB1+SLAXib8Zfg/qqOiq8B12vz1eIyJH0GOidrN/v2PiwKXLgPslji65anLBGgkn8Qm7TgKeEZ0LLknetS3xgAL1df4v3zwEiD5XP4PNWcAF4VKc2fFpPD+XWVCSNKv1Nytnb3Fdr+9COD/WDMdSMrwISebaWaK6FGMzAul1V78H+11d0FkEABgnRWz7QLUS44jMpIcvGxJpJyKEJ8Nhs09/qYbEGNVmXTDNMxzkl52fkdzHI1rM4lUOAh/Cf72jrY1rt3kRzXlodBJL7rfLsoknTIOMT1iUj0CcAF/1JA6+WIUnP16aAuqtPptuHOPjt8AAMDCuebtn9y7XmzE3v7jnXtbfOBCTf32WUoDceyULKFn3gaF86T+MhgBENVYbb1/X6fwh+cASjSQyZtrDg3gopj2+h1uBX+xBnC24LHZo1+VaJi//ncAAPbk8R7U1atnaKiyd1X9j6fhVnB1NMe5pv0TTPwIcpoRLeThcT4xenbDdCbXmKZ61EFV3wc6Kh+dO/IBAGDD1wALsBf2gqo3Vbr1yGPXdakq5H0+hvrixZ22UKGHcwAXsJEHQZAp8DvtnTfEuNtwowwCAFdV8pSWy3oI44uo8jxRfFkRkvRuD6JQqgS+4qok7MkjnlZTHfO027m10eKpsNkcb2naFgAANJsw+NxjH+tVoUyhPYIg8wUX8HOk1pRgJ9PhzkvkyUfRfizzrDYdR/E3rwbra94HPB2nztWZZ7W3ooFvxvxM/Z334o+CxOHYu8v2Z93oM5oS7DQuxQO/8as1hwYAAHtjIMa1oSVewt64xMv09mf925n3eOHWynWAUHXu2o9aseOp39Go6B+tjgIAyKrdayl835Z+6y8P0gGLDqIRf1IZxX8oJMOlywDjy5emG0zkerx8juaQtTjZhKU1AAAQuOaG0eu3pOrLc58hfxPgy5Jjf+ZZDiVGbxXWTLcRPzbPN4nMy7zdLb5fODH0Fz/7WTu+vemW7TJOAP+4TiDoUYzMD378vKpfl/TrSeN35lkAdRZ1BTXgiaUlD7+TVCj/zCYKVHaVtPQhcd1GKhNapgsyRPbGP+jek5Kxx0+YvlQe0qXRMAB1hd8voYw/C5g/jx5Mj6cOXMDPm3xDKcQunaKwlERycHx9X2DbyTqfugHA+Rv2vduKBOxh59N1+grg7HszPFNAqWr3uorE/I7pUNcJhOMx7w2dToiFP7pI10UmqLGoC1RGJQBQ9OQvHoIgJ57ANbdtrgbndzTH7MiZox5RRSm81JlnmckxZZf7Hc2hYoktBJwLhY8hdqpjXj4220wYlTMRBJkBf5uWHH7o3dTshrqPQ6dKCFOcyJLMs0zTArGsnzKUSawVV2UfS9h8CmLrHwaQOUlP2wyVhP5/9t4nRI4rz/f9ehoJWhbTLmwpF5HRXSG4ehdR4EehqaSqKR7xNmYEHeM7pLQocF5a2NjxepEvF66FUotcOGpRHghy0c5qbDw45lEPqhKeJi940MYHU/eWCF1TTEOOYHShI3FmLFKyKfcguUFe6C3Oib8ZEZmVlVmlUv0+aOHME+dEZJr65vnz+/2+brcDBZuaJhZ7abuyBEFMBFrAHwfdngtJgr/TGRRzlwrF1dKOPmjb2Xv6BAD27xh6xPv9wlgPoFb5HSWeLu9bJ0mLy8pImaquZxz6Aka6k9ukT0rWg4C+KCKJ2J5juhF6UKujsKSCxTb+3OZmJPtywJA4sbvoJe4owvA7B/wEA5Wcel0HQLc3btPBZ6H0x/UiQh7FxJRIOsNIIVTfx22uW05woiPx6d+WXawUMppgb1mOr6087V0kxGf1ynz8ZtlgniSn2BY53R7k9Cag68BRV1r1YCdB66b+emRDf4/HAMnjSYMW8NMmPv11bVM3DJ0XC+EzxEHXTiB2CO52HwP5+YUze14SuaK+9WZn406830jEf2aCDdUkP/c4rm2uG8yBQhVKCOLlI33FHiM72zE44QkVfQ/P/ZK68y3NcN33vKwMU6SEW0cqOdlmkCY/XhPxUkAexcSREa3OETqNDk+/el0n6rcTbHMivSnf7QDqki/SoXD1jAEzNN07jzncgXnEnTO2sUC8+JA8njhoAT9F0nzgry9gmw1R1GAAtPso5gDgzNxv67XbfqvbLCdcOVWpdO3Pb318Z5/X2wsCCbyF/3/80SzrTDi9q1cv+LbK2a185Ob6puXwWf7//GfbLRzeCJogiGMkNIVVq61KnsfDD2cCZZAilfCUUqNRWtd3+MR5vCaCIIhUuLVbKFzHX8666brndjuxKnKc2cymDMboxbdXI+HueTkxaUmR81lNyTjjRTARBDEUWsAfNcIHnpOXFWB2hAMv/usQqaeUaqs88EuSFhF1UOxbuhGykov4wOdlBezOhv/RHIc5jq/r2a2Aberh064nbUMvU/7U4Zn57LMRr9x/993DD0IQAbZpsEgapDtqz9GKcAwjch5kmw5mV6RDNREvAeRRTEyHaFWf0ZDkWQApU530pnQlzRwwAb7HmpisHvko0fryGU3EiYbk8WRBC/hpEzNVdr+89eFG+wlych5eeJNRlqurRS6IvIqdMxDFypPVrfWmvFosSHDtqK3yQBV6sF0RusSTmw6Pa9bbwNn5D+q1a5L/oF52lcS9587OX79Ve2cee/9UXtt2np2ducSztDJb3eYmA5RSdbVY6JmawZSLcJwR8rYIgpgkDx8gqe6mkKOHD4D9YWE+/bYL/PgQuAxAWQ6COVN3HMNwRQzvPn5118H+TNpNQ9H5jmEuRVQzHMOaA9Rq4ZBNBEEQyYgMnK0v0dkIzc2UUm05vdNA3Q2v8me9mNHEz2iCIE7uB1RtVQpZAw4IqD81FEf2/qyTq/De7zXLO7CZmwPPbMpo4k9xXWXbnn5eykUi/YkTgh9L/8re+7SGf5GhBfy0Cfwyw5y/shiqP8IsIxJjFVR3CijcKCnMcmJXIskHvvtvAMAsnfFLlQXl7H3hGD+uDzz2Pv0fTwA829vQtY3Q+2zzy0rhGtwfAQDP9rZroY/7bP9PLual7Favkl+xIAE9AJj9+xI2rMmcwxEEcSBie44Bl68AnRRHt0HysgLmb8O50R3HVLgiGmW5IRLRt/vA2eRr/eDPVazr1o9ghsZri3ircD4jdZvv6VYfv+KdxmsiXhJiSZ78JU1SiQlQqNTm/0dtwzo7X2vV5gEArrmi1ywgH05LDxGru8HDlkR1uowmPhv00svtLctRSquFYQNGcZtG2MvONjXD10+peDVnbbe9X4J/ek/b7ueuFyVkNi0u5yzrX9BotSQA7uf/Vb+zf+m/0Pr9ZEEKeYKgBfxRk5s539+/8Pc3IulG0ZltfjlZ7EfmVWBGye87PUBRS6uVS58W70du8cOX68Z9zwceeLwb8YE/3x0c0v2Tl/k+cLPXARGlv3BdfbwtBlLU6xfY9v2QD3xa64CdiWDcqCxyEB2DDMd4+j6JcRAV5L3jfKXUqM7qxvBtuUgiOlT1Elgv8UJRirlelNwmgCsflM5tiGpNeTkP9PxYArVavWIYm023UJTGa5rYt0IQxMuJbdb2nimlD2atmqb57y6oyn2WPpkpVFoNuaz7Weuh4MuMJqlYb8BvioTAZ/QK4967y2U1kjHP6uaNzYrkNr/pY24uOH2am0P7m6b7TjGrqdft46LCdC+Aa0FV7rN/t3GN1vAEMQ1oAT9FcnMS9uKnTv39J0rp49F84MOB8faW5Xhx5pIXwO4tnwdD6F9brn7hOY3CnZOwN3s5eKY/3Z8N+cCjzRD2gb8gD36WwVU2v1atrhXgeaj85/+zcvudSnBBZ/t+yAc+rTXyy1aotFo3mmWdeQ7zBEEcFZevAP0rl0e7WJHzQby5P4fMBWKDQqWqMnGBY+mbpUarImV3H7iH/CvgedKxvxuUYubq5DbLIj0T93Z60SHdpogsXRyr6eAreNr2egG58G018f1X9t5//EvjiB+GeMmwdxmU0mrxmlS8FjZ+d5tlIO/6WUGzuFfWwoffYTKM6EbzqBu1l7T4lmJZ4QJMvNieDN/e+Hf1NSncJkQyrUnmnz/6icJfxEEg/TwWSCFPFrSAPw7G8IH/8v9lAJQ3F/mVUl6+ADjAudfHeYAX2Af+8COTZeUYZDjG0/dJJOJY+m6Vl3yzTc3QTTl21COyL1tFUeqirIevSetum5rBxLGRKOCcWPh4YE+x1/WMLVwgFsET9cccr4kgCCIFe5eFfXUCpGI9soxl1k6p0ar7Zzhl3RIHKFwjyxDL+hGbYsKa0Sv+WBPVT6+YXcx7hI5hCGJa0AJ+2kzIB/7HZwDg3NG1kPX7WeDCOM/0ovrAk8M8QRw7sSJ26bOwoFZHYUkFi8XTuM3NSPblgCFxYnfRS9xRhOF3hj80L8gkhkyx3XC6PchjNR3cB4m2vV5Evk1tof9fxKHgQYgjHZKrK6EYn3XLUUoN8YbEz2+27GKlkNHkZQ8VQ4WUREJ8Vq/Mx5+AfgJghtYpeUnwzbKumWOu4env8XgghTxR/NVxP8CpQypUqirAdu2D9TsD5OcX/KMoRVE/ePsYMjPdZlk3mKOUGq3wpi53L4nCfwVkaVhr9sgEQRwparUVJn3+lV2lQirWRWe3WdY0TdNjwT1J3XlMUnganGI8HMUL2yHlIDJIK8VEJZqIiWObWohy0/d+C8tbr+tErDogLS4rfHaY0RTKHgLAEw+F9GX0ynzWSemnH83JNxbANpsj24cSxw4p5MmCTuCnSL/tAk7Mk0lRS1efei+G+MAHteXn5VmA7f/nRut2OPXI3wsNV6Gfmg887I804z4AXvhkx8/cF5+EfbX10Q7jxfEUZWH2p6gPfGprfORyt7RaoZk4QZxsYsGUeR4PP5yDla/0a9EHs8+URb8i58dsIgiCyGBg2lWo8PwgLw8oCbfbiVWR48xmNmUwRq+J6ScQ9Q3ljBfARBDEcGgBf9Q4jJcvmawP/CAT94GHfUussf1PYui+vyj/+WL+BY5z3wFywjwluxVfliMjO8zSWZfSp46A/XffzWid+eyzI3sS4kVgkj7wMA0WKdM08klMJBb/j//dgZvqA/9lWdtwAD5pjWpGZ9ffFQQWVCW0MZDVxO/tNst6d6Uhd8Z3wyBeRPhRkm+VRCdLxITIywrQ6bpud10f1QeexyamFPBMb0pX0swBE5igfkrfI3HvQDgdEycEUsgTBIXQHw+5q0H6EuBYhi4irXTDclJ94IMrdSO6fM/NSYAkajUXllQAzOJj6hZUdYQo1CG4n3/SHnzXsfRbPDgrL18cbH7u/UdWq2v+PwlbEczQzANmGRAEcXhy11sxxLTu8pVAZEYjfCDj3tsZXh9TWlxW+KGNx/dPUy/eqxU3HJxXG61Wq1VVA82QFpdzcNh9Lxmgqt5nDnJyfliTuDePKBXxqHQA/5JBc1NiCkjFFRWOpfMKcoJGCVbNSl9w52UFzs690AW2KeLtM5r4gUgQFu82y5qQvoxeA0xYP99cVgAl+OxVFQCU//LOWF8ncaw8n/8D/5d2wSt77/N/R/lURAxawB81Sl6ZAc793BPUQqVVU3Nnveaz5+c+SN4+lYr12nX/wrM5vip/8BCAOPZy231AFI+aUURIk1pq1P+PP7veld6Frm2WNU3TPn3AxxMv/ddx3LtfpxQqf/o9ALj3dh4hr4aS9BfUPB6JX5KsVvde50nKyMlvEwTx4pOXFTjWlphlusNChgQ8cdLwZpy2ud1PudI1/2HvGS59sMmd6QqVRimU7Pl8sMNzDGkS99ZE4P/jfzZYuOAUcdKJzThpAkpMGX4i/gznZ+Oh5aKdr/rXvQW2bfqak9HEj3P89HJ7y3K8Q5+sXlEmrp9ScbWk+IpvmwYDZt5evZb5BREnDlLRFwcKoZ8i3BE5GgrOQ+SV3/hy7jbLNRbMUZ89aW+sN98MWSp77u5us1wLJrPP+owBY/rA4xdfrxvMAYBHfSilpW5ZvMSjfooVys0vWjdj77nNsm7NrlwDPAuSpduVwDDFNjUmcsKyWgtxNxNuTq+UGrfHC7wiB9HR+VlmK32TRJhQCD0vuvF9kOV+fsARQ1SQD8XjX1TwiIeoZ3UvVFrXH2heKKYyfwl7PQzi3v3XJwD+tKFpG+G3/2WvUsjd23kERV0IkgEWVOW+Z/ae1lSUeBist8/QbpMNEkEQo8F94Bt1eUsLx5GrH5Q6/44b/MXDB8DT9t2ydcf3ga+0an8u1vweyge+5hTSm6Rivdb2m87O15r+Ej2jV5hp6KdUrLdkU9M03nJ2vvbFTdr+JIhpQQv4aTOYUIrA5EOcSyl+OTi+vk9w/XDNDy0HmHm7unazIMH90vxwgz0Z0wfeYUytNuoFia+VLcNRAkdRy4nZQaXAH11dKQDJHipeThgKyGyNFqLnFQJKVE+aII6cy1eA/pXLGa0dvkuYm5Ow52zsrgRG7uxvZH/Xke8WejPawAfeEtZH6d0B29S2+zEf+IS4/cEtRdvUDPbrv533fIyXbtejG4Yd70Vqk+eNzPcmq4dYvdPm14vGhW+rie+/svf+418aR/wwxEuH7wPvV68L8E+hL18B2N4f/z7iA1/bk4RKus2yvlH2DnBGbHKbZb1m2iEf+LReYaainxC1+2xTM5j6YW3+oF+iD+nniwmp6AsFLeCPg27PhSTBM00qBcXcpQL37Qy5JXN6T58AwP4dQ78Tfn8sH3ioVX5HidfG860/pMVlZaRAV9fzHZnoAVWv6yiK4jiOYxlljFyIJQb5VY5OSlqEgL7JU8lp9YGnWskEQYwH+cAH+sn3MpYoeIkgpgot4KdNfPrr2qZuGDqqrUqBCyUGC3cCsUNwt/sYyM8vnNnjPmxQFPWtNzsbd+L9RiL+MxPUWZbkWaCT3du1zXWDOVBKjQnHlxYqrYK4g25Y681FOoYniKNm5MDx4T7wPDfGj6xBZO6X7gO/HPOB7wx7khfPB542v144vk1tof9ZxMQJmWgCYTuOAR/4Usy2nW9zIr0pn+ADP3zADE2fqH66zU2WWIj5INCf5AsKqeiLBC3gjxqpUKmqzGBDFDWJM3O/rddu+y/dZnnCzzYCYi6uxIxKkhb+PPFdlgBktw4gFW6UFGaJvFSCIE4iJ8wHnnipeT7/h8RiS1SLnpgA5APvMbCHQLw8kIq+UNACfor02y7gxHLgFbV01S+vnpcVYDb1wIuXemr3UZyXZwEWWdFyV6bZ+JXS4C9JWkTUgbF990/H0rUdP3NffBL21dZHO4yHCCjKwuxPvq5ntvIfkODYzzY1g13KAecO/8QEQRwP9mR84IfdRIvdJXkUt9sZNHtPaiIIgjg4SVV9hnHsPvBT0E+/FgBBENOFbOSOGodZ2/e9tau0uKyAGeWm7UmyazfLmjZogV5YUgHHWhdXuvYwVya2K4xEeHLTBLBvidW7/0kM3fcXleRZoCfW5wAc5z7rISdsoDNbpcVlBWCGabvw3Efwp37EQ5qYDjOffcb/Jbbuv/vu/rvvHvEjEcfIwwcAmKHFEHL08IHvVZlOv+17Vk7EB/6ru07aTX1pcyxdC6smH2Xv98En+P2ecHTPaPLG1DRNtxwuSUMfmDhBxJyNs42OCeIgCAO3rS+b5bB2lptZgnmsPvBT0c/PP2HgvyHJNyVOOqSiLw60gD8ecleD+iOAYxm6UEPdsJzE/KHCjZISXKkb0eV7bk4CJFGrubCkG9eh9AAAIABJREFUAmBclDXdAreMPxzu55+0B991LP0WV/28fHGw2XcMzWyViiuqAjBD1zQ/5jbnFdYjCOIoyV1vxRCxMZevBCIznKn7wDcNYVnRaLVaraoKZnhzUKl4NYd22/sk13Pttqe5GU1BNGmjpAC50IDEywOfcaZNOrmtMTkbEwemUKnNn2Ubljtf85SzoT62apaDfMppxHH6wE9eP21T0+/8JIaszlo6yeeJ4aC6l62ixNFAIfRHjZJXfug5537uigj3QqVVw3trrP8MAHD2/NzNj9euJYi9VKzX/vLe2ja/8GxO/fU5xpwHD4FCNITebW4yzCj5facHKGpptXLp0yLDjw+Bgn+hK2+tG8zBRTEdt01uBX8xJXTdvft1SsXyp98DgHtv5xHy6sIZ75hdWVB/8u2Vs1uBQqXekE3DYj0AOA88WXiXlu8EcYKJ+cArpUZ1VjeGx5gWKq0qNEOkcarqJbBEH/h7d/nbkYxPVjdvbFYkt/lNH3Nz/W1N2wYAzM2h/U3TfaeY1SRqO9eLktsEcOWD0rmNYeWfiJeG2OSVv6QZKjEqtlnbe6aUPpi1ap4VOoRPenqKTqHSashl3dewUAnRjCapWG/Ab4qEwGf0CjN5/URzkyE//1pvTzxHVWVecXzixYV07+RCC/gpwh2Ro/LJjd6V3/j7sW6zXGPBIdOzJ+2Ndc+2MzcnYQ/iyMttlmvBadSzPmMA8Orrwa0QOhx7bbn6Rd2/R2DNzC/8xdfrBnMA4FEfSmmpWxYv8aifnL806BrqmSWvXAO8mnRLtytRe1CRip/dym8go8d/bGBqBns164vNhhxEx+Bn6U30fZ4esn3gYyhyPihT588hQz7wYg7HL3AsfbPUaFWk7O4D95B/BTxP8oFffEuxrLC88mJRMnx7zt/V16Rw2849t7iY0RTUduYV9N1m+cBZrQRBnErsXQaltFq8JhWvhX3g3WYZyLv+VGcW98qad/gdV72MaprjFdpM6zV5/ZS7DqD+7Sef1ERLXlZihZsIgpggtICfNjFTZQAIXDpFYKnil4Pj6/sE207X/NBygJm3q2s3CxLcL80PN9gT4NzrYzyTw5habdQLkmubusEsw1FKjVZREiGko01Z+aOrK9z4LcEENSjqgsxWEXxlMOFLR0FXBHECcCx9t8orLdumZuimHDvqsU3N6HBlAZ/oha9J626bmsHEsZEo4JyYARTY1AkCYwsXiNWli9pkJDdlDHhwaNvrZHHh22ri+6/svf/4l8YRPwxxAkkt3iYV6+H1PJi1U2q06v4UsKxbECrpNsu6XoZY1o/YFBPWjF7xx5qsfgITdPMg/TwaSPdONLSAPw66PReSBH+nMyjmLhWKq6UdfTBus/f0CQDs3zH0iPf7hbEeQK3yO0qFJRWMKV6+ubS4rIyUqep6xqGTcYIXy/fJbNSSHeUYpORHAPR9njpie47ptvBBrY7CkgoW2/hzm5sMatX/ox4wJE7sLnqJO4ow/M7wh+YFmcSQKbYbTrcHOb0JEfmJDEgQBJFB0hlGCqGUdLe5bjnB1Efi078tu1gpZDRBJPsEhZQsL1Y9q1fm4x9ePxfjB+69rgNgQFkJgpgMtICfNvHpr2ubumHoqLYqBaFwg66dQOwQ3O0+BvLzC2f2vCRyRX3rzc7GnXi/kYj/zAQbqkl+7nFc21w3mANxYH5o3GZ5gst3giAORfqKPUa291pwwsMDe0Sf7O58S3M5pE95WRmmSIC3nziqe9JQDj0gbXudML5NbaH/lcQYBPlBQCRaPtMzPdjmRHpTPkj2AcAt54cPmKHpE9FPvpFgrTcX60XJ9xMaE/qjOyJI904ytIA/aiSRFzpGbaQzc7+t1277L91mecLPNgJiLh5P3kpa+IcCULNa3eZOwi4GMzSWlhdLEMQJIDSFVautSp7Hww/nYKbsfun4QCzycmLQvSLns5oyBiRedp7P/yGxAjMVcyJGglu7hY6bCxWeH+TlASXhdjuxKnKc2cymDMboNUH9jNQfVUqNRmld35lUUD0xDUj3TjS0gJ8i/bYLOLEceEUtXX3qvcjLCjCbeuAV1Jafl2cRqwfCbZVn41dKg78kaRFRB8b+SBNW8I6lazt+5r74JOyrrY92RJ15RVmY/cmfF2e3pvHT4Z+YIIjjwTYNFtnpG9kV+AB14/geQeJiOzKK2+2ENgYymjIGJAiCSCFW1WckJHkWQIrYpDelK2nmgAlMXD/9bQs+uoPZFdJRgpgOtIA/ahzG64/KeXjhTUZZrq4WuSDyKnbOQBQrT1a31pvyarEgwbWH2SqzXeHfwZObJoB9S6ze/U9i6B1P+fmuAfMvcJz7DpATsVyZrcV6qxgOshUopSpNoafK/rvvpjXNfPbZUT4J8YLw8AGS6m4KOXr4ANjnu4Tp9Nsu8OND4DIAJWR/HNlxTIMrYnj38au7DvZnEm/qS5s4cvJVk4+y93vNaotL5+bAI/MzmuID0ir+dMEPnfzzKP8MavAdghhARJBvfYnORmguo5Rqy+mdBgu1i8qf9WJGE59PBUGc3A+o2qoUsgYckLLx9ZMLtNss692VKiJJT+HMgRygVqmGyJSYlC6l6R7x4vNXx/0Ap5Tc1aD+COBYhq5xdMNyEisnFW6UlOBK3Ygud3NzEiAJs6XCkgqAWXxM3YKqJoY9HQj380/ag+86ln6LV43PyxcHm597/5Hdyuf2arXVarVaraoKTDKdlSCIA5C73oohNhMvXwlEZjh5WYFjbQlTCXfYjqNAKq6oYEa5yU+ZbDOwzozhNg3Pi0nIBjM00xajXM2h3fY+yfVcu+1pbnqT26xZDrDg6dCspXvjEaeG5/N/4P8AvLL3fji+NPaSICIUKrX5s2zDcudrnnI21MdWzXKQD+1khpGKKyoca13IHQ9b4kXuMpr4bJBteiK5ZTnenDGrV5Sx9XNFBTNMm+fNP/7nYHzb1DTuINJqtRqlHPrA04H7EodmGroU1j3ipEAn8EeNkld+6Dnnfu7ZghYqrRreW2P9ZwCAs+fnbn68di1B7KVivfaX99a2+YVnc+qvzzHmPHgIFKIh9G5zk2FGye87PUBRS6uVS58WGX58CBT8C115a91gDi6K6bhtciv4izngXMJju3e/TilU/vR7AHDv7TxCXl04w7wqewvqT/e9reDsVlF5ZZlypQjipUFUkPeO85VSozqrG8NjTCOJlFDVS2C9hMvce3f525GMT1Y3b2xWJLf5TR9zc/1tTdsGAMzNof1N032nmN60ePe/9QHgvqFp/l2Y8Z7c+JT2EgmCGIJt1vaeKaUPZq1aSEIWVOU+Sy/rUai0GnJZ9zUsFHyZ0SQV6w34TZFYoYxeYcbVT6BQaV1/oBncY6zdDsbPy3mg50dwqdXqFcPYbLoF0k+CmAa0gJ8iuTkJe7EMdx4ir/zG3491m+UaCw6Znj1pb6w33+RynJuTsAdx5OU2y7XgNOpZnzEAePX14FYIHY69tlz9ou7fY07C3uzl4MJffL1uMAcAHvWhlJa6ZfESj/opXqY3v2jdjL3HA7dWrgFeTbql25XA8dQ2NSZS8bNbRRYVemZZZw4ART2MeRM5iI7Iz9Kb6Ds8nVy+AvSvXB7tYkXOB/GS/hwyF4gNRMFOAIBj6ZulRqsiZXcfuIf8K+B5wrG/tPiWYllheeXFomT49py/q69J4bade25xMb0pLnFc394db/ZJf0EnHXJIJg6EvcuglFaL16TitbDxu9ssA3nXzwqaxb2y5h1+x1Uvoy7QeB7rab3G1E/uO7/dV0qNVazr1mzgJOze2+lFP5LbjMfzjwzpZxqkS4QPLeCnzWBCKUKWaTywVPHLwfH1fYJtp2t+aDnAzNvVtZsFCe6X5ocb7Alw7vUxnslhTK026gXJtU3dYJbhKKVGi0uzbjmjFWLhj66uFIBkE9SgqAsyW8X63rF88XGYpbPuqGZWBEEcA46l71Z5ySLb1AzdlGN/sSL7ssXVzm2W9fA1ad1tk4dhVgp+AefEDKDApk4Q2F64QKyWfdQmI6PJRxgqr5IGEQQxFHuXpRx+FOvh9TyYtVNqtOr+FLCsWxAq6TbLul6GWAOP2BQT1oxe8ccaTz+9jm4z4VsYRVoJgpgItIA/Dro9F5IEf6czKOYuFYqrpR190Laz9/QJAOzfMfSI9/uFsR5ArfI7Srw2nlJaFSn5i8vKSJmqrmccOok1ttvtAFBKXiU/vq3ANkUVvoNC9pUjkpITAdB3eKqJ7Tmm28IHtToKSypYbOPPbW4yqEElygFD4sTuope4owjD7wx/aF6QSQyZYrvhdHuQ05v8unleYEBop/XA0F/QiYcckonRSTrDSCGUki62CT2dkfj0b8suVgoZTbC3LMfXVl49T0yXsnplPv6I+ol0QRyvVwr0J5YK6RLhQQv4aROf/rq2qRuGjmqrUuCSF7dABwDEDsHd7mMgP79wZs9LIlfUt97sbNyJ9xuJ+M9MsGs6ypapa5vrBnOglBqTOSKPbwVLIvJ2vNgrgiAOQ/qKPUa2X3vwZx32mJjN7s63NMPlMPKyMvwQx9tPnEzlS2GFZJuaru1QKfpTCjkkE4chXI49Ei0fnn71ug6UUqjAXbDNifSmfLcDqEu+SBcqrdbwATM0fbL6SUwT0iXChxbwR423Oh2iqEmcmfttvXbbf+k2yxN+thEQc/F48lbSwj+IyEJ2awJ5+fB18wmCOE5CU1i12qrkeTz8cLI3BuJwSYooUop8KHI+qylO4UZJYRZtIxIEMQxu7RY6bfYd0d103XO7nVgVOc5sZlMGY/Q6mH5mMF4vgiDGhRbwU6TfdgEnlgOvqKWrvrVGXlaA2dQDr6C2/Lw8i1g5kIitcrgK/cAvSVps04GxP9KEFbxj6dqOn7kvPgn7auujHVFnXlEWZn/yxTuzlf++vf3B8p/vWkw0z72atbwnpg23iCc3+NPGJH3gYRosstPnjvoUkVj8P/53B26yDzyAL8vahgNwRYqWzejs+nIFLKhKaGMgo8nHNjXj35RxI0CJlwFySCZGxqvq43bX9VF94CV5FkBKkE96U7qSZg6YwHj6KfiPHwBhgxT08rTbNjXjf83ngf90+qZxfPqUxuGnVaRLBId84I8ah1nb9721q7S4rIAZ5abtSbJrN8uaNmg/XFhSAcdaF1e69jBbZbYrnEB5ctMEsG/5ci4+iaF7ds38h6PnucQBjnOf9ZAT7qeZrdLisgLnzoZYvQNwnHYbmFui+lFTZeazz2iJTgwwIR94AFBC9sd8x3EIXAy6Id+479OdhPdqxQ0H59UBH2NpcTkHh91XPUt39T5zkJPzmU1us+wrr20aDPjpBzpAImIOydx1mQzhiSjCgV3nFeQ8H/gSrJqVvuDOywqcnXuhC2xT08pNN7OJn9Hs+pPEkHJl9BpgHP0MjfuPd6JVdMLazfXz2X6P9HNqkHM7QQv44yF3Nag/AjiWoWsc3bCcUHWngMKNkhJcqRvR5XtuTgrm1oUlFQCz+Ji6BVU9fEC6+/kn7cF3HUu/xX9G8vLFwebn3n9ktkryq0l3/KE78pEdQRAvGHlZgWNtiVmmO2zHUSAVV1Qww5tx2mZgnRnDNf9h7xkufbDJnekKlUZJCWa1zwc7PEd2E99O3bW96Sewv5/36nsSRGzdTst4Yhj8RPwZzs8uJsqIWPWvewts2zSYKHKX0cRng2zTE8kty/HmjFm9ooytn+CbAtHjHP/ezDBtTz8fOcm3JghiElAI/VGj5JUfes65n3u2oIVKq4b31lj/GQDg7Pm5mx+vXUvQPKlYr/3lvbVtfuHZnPrrc4w5IoIpHELvNjcZZpT8vtMDFLW0Wrn0aZHhx4dAwb/QlbfWDebgojhPs01uBX8xB5xLeGz37tcpRcuffg8A7r2dR8irC2e8Y3ZlQf3pvhf0n91q77YjFfoUpbQiCtITBPGiEAqh5zryfZDlfn7AEUNUkA/F419U8IiHWGZ1L1Ra1x9oXhqnMn8Jez0M4t791ycA/rShaRvht/9lr1LI3dt5BEVdCJIBFlSF681ielNRKtZbsqlpmjfYzNtVmn4SBDEC3Ae+UZe3tHAOuvpBqfPvuMFfPHwAPG3fLVt3fB/4Sqv252LN76F84MexF9KbpGK91vabzs7Xmr5QZfQKM6Z+cts6gymlxn9tl2t70UELlVYVmuEZAp9XJ1TnmCCIBGgBP0VycxL2Yhnu3Ohd+Y2/H+s2yzUWHDI9e9LeWG++yVOYcnMS9iDO1d1muRacRj3rMwYAr74e3Aqh6NbXlqtf1P17zEnYm70cXPiLr9cNHrD+qA+ltNQti5d41E/xMr35Retm7D23Wdat2ZVrgFeTbul2JXA8tU2NiVT8zFbun/pfa5OS+u+++24yA50OfpbeRN/kaePyFaB/5XJGa4eLTG5Owp6zsbsSGLmzv5F90eJi481oAx94S1gfpXcHbFPb7sd84BPi9gcVyTY1g/36b+c9H+Ol2/Wo3nS8FxlNhRslhe0sN+rylmZ0/nrUL24A+tt5ybjwbTXx/Vf23n/8SyOxiThN+D7wfvW6gGvef1y+ArC9P/59xAe+ticJlXSbZX2j7M3/Rmxym2W9ZtohH/i0XmHG10/fBx6+0ofIywoUTz/lMb5HzonWz4w5FU74RyNeKCiEftowQwujG5ajBGGZPLBUUasiaapRLYXjTgNc80PLAWbe5lc2PlDPAwDOvT7GMzmMzVYbrVarUeUB/F7WVqOkgNchGQ5/dOFlkmSC6hV1GdYKAIqcd+2mWeZfUrls2hQ+TxDHQkyyBupx+ESN3OO6wR3dgwjKIEI9q/ugD7w60kMP+hgP4nR7WU18mHULFDZPEMSBOJQPvCc4UnG1pIj5X0aT8IFfCeVhevH0Wb0yH39E/Rw6DOknQRwVdAJ/HHR7LiQJnutxKSjmLhWKq6UdfdC2s/f0CQDs3zH0iPd7LGx1RNQqv6NUWFLBmK/30uKyMlKmqusZhx7+2NztdgDsrOuOf2PHYYbeGdeA+Y033jjsM50mUjIjAPomTymn1weeTz8bk5h+0t/Oy8a3qS30/5oY5BT6wJN+cjLmVDjhH414oaAF/LSJz4Zd29QNQ0e1VSmInc5B104gbKQEwO0+jqSJQ1HUt97sbNyJ9xuJ+D5xMJNO8nOP49rmusEcKKXJJTg5DtRqQ+xjiBtYW3aRMqgI4qRywnzgJzj9JF46ns//IbFkHVWBJgDygZ/o8p0giBGgBfxRIxUqVZUZbMiWaBJn5n5br932X7rN8oSfbQTEYZoSOx5PWvjzxHdZApDVKpxLgygESIXKaqmjW53YLgZBECcGezI+8MNuosXukjyK2+2EfeCTmtx7Ow4cJzL3tXRtZ9xIIIIgTg9+UuAB5izH7gM/nn4mQvpJEEcMLeCnSL/tAk6oCjMAKGrpqu9snJcVYDY1YjWoLT8vzwJeFVAOt1WejV8pDW4F88Xy7OE/kP2R5x3iWLq2o1aDVXdeVsC+2vpoh3mV5Bdmf/L3bDNbz/z5bNKeMXCWfx6CII6Ghw8AsJhk+WFEoSr06fTbLvDjQ+AyknzgZ7MfgAd8hsXrq7sO9mcSb8rzNuEfOfnRTnyUvd9rlmd9OTcHHpmf3iQV6q1iNO4VIBskwocftvvn8HT2ToSQiiuqZVhbX6KzEcpDVEq15fROeVmJzetsUzM6pUa9mNHk+cB7Z0C8nHC1VSlkDTigY+PoZ5j/+AEQNkgQKVOkn1Nh/91305pmPvvsKJ+EeKGgInZHjcOs7fve2pXXdTLKTb9mm2s3y0mFowpLKuBY6+JK1x5mq8x2hROor9KHxb4Vdf50mKF7ds1816DnucQBjnOf9ZAT8/fM1twvE23ggVd/OVh7miCI6ZK73oohNhgvXwGkhILwyUzdB75pWMKLqdVqtaoqmOEJp1S8mkO77X2S67l2G7mrRSm7ya+bL8abaJYQcZLgHu9pMfP839E/FfFCU6jU5s+yDcudr3nK2VAfWzXLQX75xfOBH08/fex/vDOQ6036CQCY+eyzjH/H/XTEywOdwB8PnhjyXVtmGZEkqaA8c0DhRklhlhO70h8vbCPHK9MxS2f8UkVVFXbYNbz7+SftwXcdS78lt9YKQF6+CDyKNT/3/iOrVbr5ReOvgypXHufVtZu0d0sQJ5SYD7xSalRndWN4jKnwEhYhOap6CSzRB/7eXf52JHqH1c0bmxXJbX7Tx9xcf1vTtgEAc3Nof9N03ylmNY0SKEq85MQW7fwlLdeJ4dhmbe+ZUvpg1qppmv/ugqrcZ+miUqi0GnJZ9zUsVDQpo0kq1hvwmyIh8Bm9woyrn+KTakmnQqSfBHGU0AL+qFHyyg8959zPXREkWqi0anhvjfWfAQDOnp+7+fHatQQJlIr12l/eW9vmF57Nqb8+x5gjIpjCIfRuc5NhRsnvOz1AUUurlUufFhl+fAgU/AtdeWvdYA4uivM02+RW8BdzwLmEx3bvfp1SWvPp9wDg3tt5hLy6cMY7ZlcW1J/ue7Fc2a3850g21zeZX4k+X/q4Qr8DBHGkZPvAx1DkfDCR8+eQIR94iHofAADH0jdLjVbwV53cfeAe8q+A50k+8ItvKZYVzj/ixaJk+O4ev6uvSeG2nXtucTG9qSj1ug7UlYJ4dr+yM0EQxBD48fNq8ZpUvBb2gXebZSDv+llBs7hX1rzD77jqZRSKG82jbtReY+on9503mFJqrGJdtyI+8KSfBHGU0AJ+ivCpbHT/07Wb64al/GYxpIw1FkSJPnvS3lhvvhmaC3vn6m6zXAvCSZ/1+Zn6q68Ht0JomvvacvWLun+PYE7NL/zF1+sGcwDgUR9KaalbFi/xqO8Vnosi3fyidTP2Hs+8WrkGeDXplm5Xgl8u29SYSMXPbuU3KFTqhYrY21VK1fEzp7777rtxu55GfpbeRN8kkYFj6btVXmnZNjVDN+XYUY/Ivmzxv2W3WdbD16R1t03NYOLYSBRwTqyJHNjUCYKqmS4QOwmKVtlMaXK7HSjY1DSDt6TtKowC/e2cRC58W018/5W99x//0jjihyFOFPYuS5k7Fevh9TyYtVNqtLzZGZc4oZJus6zrZQjZGbEpJqwZveKPNZ5+eh3dZmxE0s9pQZM0IhHKgZ82zNDC6Ibl+LbrXmaoolYbImWqWgonjga45oeWA8y8za9sfKCeBwCce32MZ3IYm602Wq1Wo6oCjmVY4HlLjZICXn50OPzRhRmp2+0M7PX6VVmHtEYH3WRU+IQgjpGYZA3U4/AJUn0KS+rAX7Pb3IxkX/KCH7t2dnfeq1rxCyNV1ZEempf6EENye84BnG4vqwm9rgNndsXPX13e0TM+PUEQhCBpkpNCSBTd5np4QigVV0uKmP9lNMHeshx/GF42RCTEZ/XKfPwR9TML0k+COFLoBP446PZcSBL8UKWgmLtUKK6WdnRrwGSu9/QJAOzfMfSI9/uFsR5ArfI7Sjxd3td7aXFZGanUlGubuuUEE+0JYW9ZTmIFgIPwxhtvTOp5TgMpmREAfZOnlJSkyUGysx2DEx7hPcn7ZHfnihgud5yXlbgF5SCeHB3OsMh3bubwAiWbzRuFMUalv50TybepLfQ/lDgQ0Tzx0Gl0eJXf6zpQSqECd7wA/K5dKSC9Kd/twDs7ASLh6hkDZmg66eeLDU3SiERoAT9t4rNh1zZ1w9BRbVUKYqczyUItVurJ7T4G8vMLZ/a8JHJFfevNzsadeL+RiO8TBzPpJD/3OK5trhvMmUKJUR6ERsfvBPEyEJrCqtVWJc/j4YdzsDJIfHsgEq6ZlxOD7hU5n9WUjBP2tCNebp7P/yGt8vzRPwxxkhhw7/WXs2667rndDpI8dGczmzIYo9fB9PPAkH4SxLSgBfxRI4nCTkO2RJM4M/fbeu22/9Jtlif8bCMgDtPi2U1JC/8gpQrZrR58/b402U0BgiCOA9s0WEQoRknMAcCD6bMr1YduosXukjxKtD5yRhNBEMTB8ZMCR5QuQMybkJIqnt6UrqSZAyYwnn4SBPEiQAv4KdJvu4Dj2SgJFLV09an3Ii8rwGxqxGpQW35engWCqu2ACDb1dlbDVegHtoL5Ynn28B/I/kgTVvCOpWs7ajWI/c/LCthXWx/tiDrzirIw+5O/Z5vdCgCu3fx9nQFghtZRS6sVOoafFvvvvnvcj0C8oDx8AIDFJMsPI3r4ANjnIpNOv+0CPz4ELgNQQvbHEcFKgwd8hsXrq7sO9mcSb8rzNuEfOfnRTnyUvd9rlmd9OTcHHpmf0cSLcl5X2bb38S/laEfxtMEP2wfN5OgQngAQFImLLHndZlm3nip4ZG3ZxQMEJuZlJTavE5U/68WMJj7FC86AuHJVW5VC1oADAjqOfgZjCnMRw1wSvUJPIS751xnSz4lwUPd4X75ItV5uqIjdUeMwa/u+t3bldZ2MctP29lRdu1lOKhxVWFIBx1oXV7p2cz07WZ3t8qImgUofFvuWWL37n8TQy+ImfNeg57nEAY5zn/WQE/P37FbANnXDaj/xB7ao+MmxMPPZZ+F/x/04xLGQu96KIWakl68AUoKjWzJ5OVyP0x0mWAJekcnwhMU2A+eNGG7T8LyYWq1Wq6qCGZ5sSMWrObTb3ie5nmu3kbsqLCvTmqTF5RzYv8ArKPr2zJ/6uPS/0fzzFDI48X1l7/3E6HqCELy6XCspzND8WREA29R0y4nsZIaRiisqHGvd62Gbhlf5M6MJhRslxa9bB3vLcrzKQVm9ooypn4FNSKOkAHNzfq9wlVLbNBjwdD9/yIJGxAGJyRSp1ssNncAfD54Y8jofzDIiSVJJZdwKN0oKs5zYlf54YRs5XpmOWTrjlyqqqrDDruHdzz9pD77rWPotubVWAPLyReBRrPm59x+ZrVzsL+bwqK9WW5UCt9oLdnYJgjhxSMV6tasZ3nG+UmpUZ3VjeIxpodKqQjOmBPEYAAAgAElEQVREGqeqXgJLKn7s3rvL345kfLK6eWOzIrnNb/qYm+tva9o2AGBuDu1vmu47xaymXrePiwrTvQCEBVW5z/7dxjWSIYIghpIr1ltF29TCOehDaoIWKq2GXNb9HqHLM5qkYr0BvykSAp/RK8z4+unZhLhdABf+rqq2RaU6qVhvyaamad5orx3GDpggiCHQAv6oUfLKDz3n3M9dESRaqLRqeG+N9Z8BAM6en7v58dq1BNWTivXaX95b2+YXns2pvz7HmPPgIVCIhtC7zU2GGSW/7/QARS2tVi59WmT48SFQ8C905a11gzm4KM7TbJNbwV/MAecSHtu9+3VKJcyn3wOAe2/nEfLqwhnvmF1ZUH+678VyZbZy/5W3l3DnjohLSCvFTxDENLl8BehfuTzaxYqcD0Ip/Tlkbk7C3iwfQtT7AAA4lr5ZarQqUnb3gXvIvwKeJxz7S4tvKZYVzj/isa0yfHeP39XXpHDbzj23uJjeJO8yKKXV6HNEHJwPANnznmjIEJ4Yl3hipG1qmohhd7sdKMuLPVPTw7rn+XXYpmbwBCalVF3eMXaWG/WBpmgv29QMLGNd0xwAiqqCMZQaYYt329Q0NrCMP5R+Lufh24y4zXDQvtg+2FkuzVpWZ/wvkfRzDEi1Thu0gJ8ifCobFU5+uqz8ZjGkjDUWRIk+e9LeWG++GZoLe+fqbrNcC8JJn/X5mfqrrwe3Qmia+9py9Yu6f49gTs0v/MXX6wZzAOBRH0ppqVsWL/GoHy8tx5FuftG6GXuP5zytXAO8mnRLtyvBhNc2NSZS8bNbAQB//VY9foMDFoTxIOnP5mfpTfTVEaPjWPpulVdatk3N0E05NkcU2ZctviJ2m2U9fE1ady9Cs1LwCzgn1kQObOoEQV1MF4iVXIpW2Uxs8uo0xWrn0xYiQRATY2Tdc6As8y7ZQgpm7ZQarbpo7DArmgO/yxJjOietnwK3uW6h1CjKWyM5jhAEMTa0gJ82gxWhoJQafmHm5rrlQPHLwfH1fUIhFNf80HKAmberazcLEtwvzQ832BPg3OtjPJPDmFpt1AuSa5u6wSzDUfivA//dGG3hzB9dXeH1S7qdAZeRoCorMlt5RRZrvSmvFv0vwXJSpu0EQUyVmGSlr2KDaWFhSQWL6YaItfQnkgOGxIndgwhNwA/D7wx/aF7qQwyZUrXT6fYgZzTxz94pNVotiQ+pa+aYa3iy5z3ZkCE8kUGCU9vIE5aRdG+1tOMlSw4V0nCOO0/KDK3g+fo9OQM/xAT0E1KwfJdwyBpG9Fc2DqRapwxawB8H3Z4LSYIfqhQUc0+NHu89fQIA+3cMPeL9fmGsB1Cr/I4ST5dXSqsiJX9xWRmp1JRrm7rlBD84hyI7vf/AkFRlk5IKAdBXRwAHOHfOthUKTnjEeRLvk93dDSI0BXlZiVtQDuLJ0ajuSen4WujNhXl65yFHJU4YZAhPZBFP+cnweh9gJN0LnWsPFdLo2UhhSQXzz4Ds3cjiP4UJ6WewfCeOA1Kt0wYt4KdNfDbs2qZuGDqqrUqB73QiaTM3dgjudh8D+fmFM3teErmivvVmZ+NOvN9IxA7DQ78oSX7ucVzbXDeYA6XUmFB8qVSsN2TTi+pX1Orqje76ZFbzBEEcE7Fg9Pyok9yD+Q3zWW1kRp2XEw/DFDmf1QQgoVq0E/a0IwiCmA4R3QtvXB5MSPmByK5dKRTE4X3mNG1i+knLd4I4WmgBf9RIorDTGBXazsz9tl677b90m+UJP9sIeMlZsQ3opIV/kFKF7FYAkAqVeiuUIb/rJOfiEwRxIrBNg0WEws2+PuAAxS/41DaxBF5kFC/BPaNJwuzQzUvi9BAzhKdTLGLKRGSp13WAZWAMIfVj7PPdHUcprWbMMyeon+69HQeOEzmNsnRtJ6U8KTEVSLVOFbSAnyL9tgs4sRx4RS1dfeq9iNcrTRoA7T6K8/IsQqU+ARF05QVShavQ85zy8LlRSkrTgbE/0oQVvGPp2o6fuS8+Cftq66MdUWdeURZmf/IPtvJyqFCpbWoGPih1opEA3rk+AEWZe9UZjBMgDsX+u+8e9yMQLzoPHyCpbIcII3r4ANjnIpNOv+0CPz4ELiN6oB0RrDT43DMsXl/ddbA/k3hTnrcJPyPVj3bio+z9XrM868u5OfAI1Yym/p/PJuW2Ps5+YoIgThHhuVb2mxy32xk+6IDuxXodTEjFCr4pdxxleTVVrSern1KBW+gZkR+PRAt64uAcaE1O6/ZTwl8d9wOcOhxmbd/3VqfS4rICZpSbtrep6trNsqZpZrwESGFJBRxrXVzp1XlLh+02+Zi+Sh8W+5ZYvfufxNDL4iZ816DnucQBjnOf9ZATPzuSPCuefo8/zYP/z3L8HyW3WdY03auDDzhOuw2/LzF1Zj77jP877gchXgRy11sxxAbj5SuAlODolkxeVuBYW0LJ3GGCJZCKKyqY4QmLbQbOGzHcpmE5AJRSo9VqtaoqmOEJp1S8mkO77X2S67l2G7mrRSmzab5WLymAWhWf+oNLAGbe/h3VoT+VvLL3fjihNPaSIFLgJyi7QvjsrYPontCviFqOIaR8NMtyBlOCfCaun/Aq5nnjTTDL8jRDQkSkQSfwx4OneLxOEovVb0sy/ciu9BaxkeOV6ZilM36poqoKO+wa3v38k/bgu46l35JbawUgL18EHsWan/tPz/MGLKPGX/f7ob1Z6dLMWTjPYn3PHPKBCYI4RkQF+ZB3cXVWN4aHxxcqrSo0Q5yEq+olsF7CZe69u/ztyKE5q5s3NiuS2/ymj7m5/rambQMA5ubQ/qbpvlPMaoJUrLdkU9M0b7h8ae0mbSMSBDE6hUqj1NGF8KnVRqmj74zSq1WFJnoppZLqCB/1sYSUTwIzzr+noZ/RQHuCIKYLLeCPGiWv/NBzzv3cFcFShUqrhvfWWJ8vYM+en7v58dq1BAmUivXaX95b2+YXns2pvz7HmPPgIVCIRnC5zU2GGSW/7/QARS2tVi59WmT48SFQ8C905a11gzm4KM7TbJNbwV/MAecSHtu9+3VK9fKn3wOAe2/nEfLqwhnvEF5ZUH+6Hw36D3F+rvqxvzfr/mn/GS4q+UdODwjq86X1JQhiOly+AvSvXB7tYkXOBxGTfhplbk7C3iwfQuzbAQAcS98sNVoVKbv7wD3kXwHPE479pcW3FMsK5x/xStAyfHeP39XXpHDbzj23uJjeVPQEuVURY5WGlm9O5bvvvhuzJ/ECcOHbauL7r+y9//iXxhE/DPEiETksSXrTbZZ1a7baqgthsk1DZLMDGFH3gL90gMdd8SJTSDGLe2WNn6arKhhDqVEvAoC6FDeYD5iGfva6vrEwCpVWa5SvMwXSTw4JEZEBLeCnCJ/KRoWTG70rv1kMyV+NBVGiz560N9abb4bmwt5Phdss14Jw0md9fqb+6uvBrcI/Kq8tV7+o+/cI5tT8wl987eWbP+pDKS11y14A+6N+cvE46eYXrZux9/gP1co1wKtJt3S7EipEZ2oslIovq9VGpSDxn5O/+bvQ5jHvu/LJZ4VQ3407bMzyzyT9ifwsvYm+MWIMHEvfrbZaFfA5om7KsTmibWpGp9RoFb1MGT18TVr38HxTuDMlFj4O3JUEQV1MF4idBEWrbGY0iSffspwR3JcIgiAORLLuiTW9p3vmis6AizLvki2kYNZOqdGqi8YOs3bu7WGHKaWGZySXFNM5ef10ux0o2NQ0sbBM350gCGIC0AJ+2gxWhIISWG3whCbFLwfH1/e+iWeAa35oOcDM29W1mwUJ7pfmhxvsCXDu9TGeyWFMrTbqBcm1Td1gluEo/NeB15gfrQg0f3Sx3+p2OwNV5/KyEqpXWihWkkYZqS9BEEdGTLLSbeGDaWFhSQWL/b0KCyN/BufXRvbcNxK7i17ijiJ6tDP8oXmpDzFkStVOp9uDnN4U7Bbau8w7vhqXN9544xC9iePm29QW+j9LJBW7TNlnHCRR9/JyHugF2pufzz/Z42mEQ4U0XClOkl8F2lbNUat8vc/X70PrCU1CP9F14KgrXuiB2yzrWjf11yMb+isTkBAR6dAC/jjo9lxIEvxQpaCYu1QorpZ2dGvAZK739AkA7N8x9Ij3+4WxHkCt8jtKPFNKKa2KlPzFZWWkkiuubeqWE0y0XyRI1xJJyYAA6BsjIqSv2GNkZzsGJzzCe5L3ye7OFXE5tJsX9kNOxZOjiRz4uM3N5DMr4tTwfP4PiZWiqLwzAQweLrvZ1uwRUnSvFx3UNrW9DoARhDRy+lFYq6qawbztR3uXjRBKNBn95NlHPrzA02bzRoGO4ceGhIjIgBbw0yY+G3ZtUzcMHdVWpcB3OpG0mRs7fHa7j4H8/MKZPS/FXCSKj/VMcYO24BclKZw0jmf4RiVGCYJIJ+QppFZblfyok9yDlUHis9rI5DcvJx6GKXI+q8mj13VGObMiCIKYHBHdC29cHkxIeb3jXbtSKIjD+8xp2sT1M4ozZiYkQRDDoAX8USOJeiQDZ+zDOTP323rttv/SbZYn/GwjIPaA4xvQSQv/IKUqm8P0JQhickzSBx6mwSJC4Wb0ihCJxf/jf3fgJvvAA/iyrG04AI9pjYZrdnY/0nzjywVVCU2QM5rc5rsGA2Dp2g6lcJ5m+BnXgeyXiVPAQX3gRyeie72uI0rf2QcVUj/GPt/dcZTSasY8c+L6Gd5swNzc0M9MDIWEiEiDFvBTpN92ASc2G1bU0tWn3ou8rACzqRGrwa/CvDwLRGu6u/d2gmyk8O8HtyEN73umpDQdGNuXbcfStR0/c198EvbV1kc7ogq9oizM/pS8MfsYgCieH+obfDbb1Ax2PmtTlxiDQZv3/Xffjf1HxsXEqSF3vfXpO0kNl68AnZF94AEgbEMcEaw0+NwzLF7fP029eK9W3HBwXm1sVnhxTEPjkU3S4nLOsth9L/yJTypzy3lkNYkXMzPA/15tVWBq46dwEieDodNimi4Th8PtdoZfNKB7sV4HE1Kxgm/KHUdZXk3dUpiwfvLDnYulRqslAXCb/5du9TB3qFIihMdEhIh2AV4y/uq4H+DU4TBr+763OpUWlxUwo9y0vU1V126WNU0z7Vi3wpIKONa6uNK1m+vZyepst8nH5NVJJoB9y990FZ/E0MviJnzXoOd5yAGOc5/1kFuOB6K6zU8H7OQleTb4bLZpMABPIj9ZBEGcMPKyAsfaEkrmDhMsgVRcUcEMT1hsM3DeiOGa/7D3DJc+2KwI+7dGSQHbFfd7PtjhObKbeO67+p/2uToXKlUVbLM5cuAAcZJ4Ze/9cHJp7CVBjAs/QfGEyN46iO6JiV9ELccQUj6aZTkZs6iJ66d07hUAr57z6jktXp1BsikxceSQ3L2U0An88ZC7KqrGFVdUy2CWEclpSiqgxBObnNiV/nhhGzlemY5ZOuOXKqqqsMOu4d3PPxlYeQOOpd+SW2sFIC9fBB7FmqNSz5Pn49fAdzmNfLa5FYpeJYgXiVAIPQ/5+T6Ilzw/UFBTVJAPRSBdVPCIh4lmdS9UWtcfaF5hEGX+EvZ6CQ/j3v3XJwD+tKFpG+G3/2WvUsjd23kERV0IkgEWVOU+Ez7GaU1y1wEW8Dgyc46GPREEQWTidjvAeU9g1OoHl9hGb7hsct0Tvc7Oq4rD3HYfRSlLSPHwAfC0fbds3Yn5wC+pYExdKUopPvBT0M9//xMU9UIwoFpttej4nSCmBi3gjxolr/zQc8793BXBUoVKq4b31lj/GQDg7Pm5mx+vXUuYMErFeu0v761t8wvP5tRfn2PMEbHo4RB6t7nJMKPk950eoKil1cqlT4sMPz4ECv6Frry1bjAHF0VArG1yK/iLueQ9U/fu1yllzJ9+DwDuvZ1HyKsLZ7xDeGVB/el+PDAeilpVHxhs4FCtUGlUYRjMn6kv/B0pP0EcMZevAP0rlzNaRQh9bk7CnrOxuxIYGrO/kSWvZfYy4DkYebmbbrOsW6Igcnp3wDa17X7MBz4hbl+6+UXrZuQd29QM9uu/nfd8jJdu1yvhRtbxXqQ3Kf/5Rv12muHlQfjuu+8mMAoxHS58W018/5W99x//0jjihyFOFJHDktQ3/yZYMNsm8HyYbCbq3vlf8AHThRSXrwBs749/H/OBd4syhBNmig/8xPXT7XaA2aXbK/B2J8Yp9ORB+jlBSO5eVmgBP0X4VDa688mN3pXf+IFNbrNcCy1onz1pb6w336wH01zvV8FtlmtBOOmzPj9Tf/X14Fbh34/Xlqtf1P17BHNqfuEvvl43mAMAj/pQSkvdsniJR/3k4nGDci9+SGZXrgFe1bml25WorodS8WW12qgUJNgmWMIaQQJ6vFoWTM1gryZ/paNA0j8iP0tvou/wFHNafeAX4wfu3CSEqigTBBFh4j7wX5YNhtx1T/fkC4CDczJwUB/44opqGTt3PwdTSg1//X4UPvAyAGZonSAJvqxrJhURIYhpQQv4aTNY0hlKqeHXE22uWw4UvxwcX99bW3Yxpnqu+aHlADNvV9duFiS4X5ofbrAnwLnXx3gmhzG12qgXJNc2dYNZhqOUGq2iJMqQxCbiKfBHV1cKgNh9jVWdy8tKqLBqoZh5rmWbBhO+dPH0f4IgjpDT6gPP577WenOxXpT8ahxj8sYbb4zfmZg236a20P84YjgT94H/EcDZ/rambYsbqAtgjwEc1Afe3mUA7tyZq7aK0tH6wAOAUlr1xjiUDzz9GU4SkruXFFrAHwfdngtJgjddLQXF3KVCcbW0o1sDsUe9p08AYP+OoUe832N5pyOiVvkdJZ4u74uutLisjFRyxfUUfzK7q2L5PplUU5KkEUnJiQDoOyQmwYnzgS9UWlVoBj9cU0qNRmld3yEzjJeP5/N/SKzhRMWZiePj1+GdU7dZFgv4gwlpodJqyGXdusAHOWIf+MGieeQDf/yQ3L2s0AJ+2sSPs1zb1A1D53YdPEYzKRordgjudh8D+fmFM3teirmivvVmZ+NOvN9IxA3agglzkid7HF6LzoE4MD80brM8weU7QRAvAge2L/YZLQRI3ESL3SV5FJ6g6fsYpzcVKjw9VYzuYHaFdIkgiKlzDD7wE9RPCbND544EQUwQWsAfNZIouT5GfY8zc7+t1277L91mecLPNgIiiCuu+EkLf54WP/QwjbuaxncxmKGxxJ8VgiCmxcMHSMr6EbuQoSr06fTbLvDjQ+AyJuID/9VdB/sziTf1HTJFRqq/WcpH2fu9ZnnOGXNz4JH54Ru4zbLeXakiErQfOu1CDsOOr4iTCj99ImNk4oCECwZnv8l5cX3gx9fPxKb+n88m1QZ4DOL4Ibl7KaEF/BTpt13Aic2GFbV09an3Ii8rwGxqymnwqzAvzyLmaBSR8fDvB7chDc+BU2qSHBj7I01YwTuWru34mfvik7Cvtj7aEVXoFWVh9qfBs36AC7oonp/NT4d/YiKDmc8+C7/cf/fdxP9OvJh4ecldb336TlJDqAr9CORlBcwv53EQH3jLMMpyQySib/eBs0lXuk3DcuCno9qmZhgaj2yCVLyas7bb3if5p/e07X7uelEKbmAuVWFYDub+2Wh7SaJi7c7nsW7zPd3q41cjflriROJPZGlqS0wIzween9BwH/ih9e18WRJV6NeDXmMIqSjnYUEppa7fx9bPtCapVi+Vdcufz375f2sbf5p5+3e0A3pApqdFJG4vGbSAP2ocxlVTzsPbzjTKcnW1yJfCvIqdM1BHiierW+tNebVYkODaw2Sc7YriIf4262Gxb4nVu/9JDL3jHZLzHy3mX+A49x0gN1D91G1+GrOTl4r1VjFcnUWglIaWXiEI4kUlZl+slBrVWd0YHh4fSUSHql4CS/SBv3eXvx059GF188ZmRXKb3/QxNxcUhZqbQ/ubpvtOUfINl7l9TrsdaG1ezgM9BA7O1SuGsdl0xynCRJwUYtmh/CXNdImxKVQapY7uq0ij1NF3RunVqsLzgVdKJdWxOgDGFNJC4AOfzLj6mSWtUrHekk1N07zh8qW1m6Sdo0NaRBwIWsAfD7mrompccUW1DGYZkaIkCa6dKNwoKcxyYlf644Vt5Lh2M0tn/FJFVRV22DW8+/kn7cF3HUu/JbfWCkBevgg8ijU/jw5hm+sGi18DiGgCbyJtm5rBJlEQlSCIA5HtAx9DkfNBvLmf7xLygYdIFwIAOJa+WWq0KlJ294F7yL/yXZQjSItvKZYVDl/ilaBl+MVBf1dfk8JtIoLJbZa3+0qpsYp13ZoN6nC693Z60QdxmzFjOYIgTjdDfeC5vW61Vfd94A2RzQ5gRN0D/tIBHnfFi0whxSzulTV+LqSqYAylRr0ICB94b0IVOxIaUz8XM6UVXhERPhadwRDENKEF/FGj5JUfes65n7siwr1QadXw3hrrPwMAnD0/d/PjtWsJsicV67W/vLe2zS88m1N/fY4xR8Sih0Po3eYmw4yS33d6gKKWViuXPi0y/PgQKPgXuvLWusEcXBS/Q7bJreAv5oBzCY/t3v06pWj50+8BwL238wh5deEM86rsLag/3Q9NfkW1FLWqPjBCvvecXjfmHUUQxAuOY+m7VV7yzTY1Qzfl2BzRNjWjww0qwSd64WvSuofnm8KdKTEANXBXEgRFN1wgVss+XKTD6+g2E0ZN7XVAvvvuu7H6EUfKhW+rie+/svf+418aR/wwxGkgWfci6TtwzRWdARdl3iVbSMGsnVKjVReNHWbt3NvDjvCBF0bwg0dCY+vnCCJpb1nOCO51GZxC/SQtIg4KLeCnCD+Liu588hB55TeLof3LWmhB++xJe2O9+WboMMvb63Wb5dq2f+GzPj9Tf/X14FbhXeHXlqtf1P17BIdi/MJffL1uMAcAHvWhlJa6ZfESj/rJheekm1+0bsbe4zvNK9cAT/yXblcCt3fb1FgoFV9Wq41KQYJtgsUO+XgdU/TMss74PnLC783InELpnwg/y2ylb/XUECtil24LH0wLC0sqWCyoU1gY+ZM4vzayV7wzsbvoJe4ookc7wx+aZwqJIVOKfgxxNBqvF0EQp42EYm0jJLqLy5J0L5a+g/x8/sneGQAjCClCkfKS/CrQtmqOyn3gxfo9nso4wKj6KY8ikvYu847/CYKYGrSAnzaDJZ0RskzjFUkUvxwcX9/7BUsCXPNDywFm3q6u3SxIcL80P9xgT4Bzr4/xTA5jarVRL0iubeoGswxH4du7PBV9NBcn/ujqSgEQS/BYxbq8rIQMRwrFStIoAMTvhWP5m4wOs3TWTV03EAQxPdJX7DGy/dqDE55whYvZ7O48eDMcjJOXleGH4LapWw5epLybN95447gfgRiBb1Nb6P8gkUo89N3NsmaPkaJ7sfQd29T2OgBGENLIxKuwVlU1g3nLZ3uXjXAUPlH9dJubyWf+B+E0/vWRFhEHhBbwx0G350KS4OcaBcXcpUJxtbSjWwMmc72nTwBg/46hR7zfL4z1AGqV31Hi6fJKaVWk5C8uKyPVinY9xZ/EGps7piglr5If31Zgm6IK30EhsRuPlBQJAX2rxEEJObKp1VYlP+okN3tjIA6f1UYmv3k58TAsyRMjxHi9iJPM8/k/xApH+e8f/cMQp5uI7oU3Lg8mpLxc0q5dKRTE4X3mJO1g+jmCSPa6zihn/kQM0iLioNACftrEj7Nc29QNQ+d2Hb1uggU6ACB2CO52HwP5+YUze16KuaK+9WZn406830jEp6TBD8coSZ+8Fp0DpdSYzBF5PBlLEiVbqHoUQZxYbNNgkUMld9Seo4UAiZtosbskj8KzdEbYGBivF0EQxOGIaE+v64jSdwcWUj/GPt/dcZTSasYsbTz9zBRJHj9PokkQU4cW8EeNtzodOGMfzpm539Zrt/2XbrM84WcbARHEFVf8pIV/UBPl4KTs8xIEMUUePkBS1o/YhXz4ANjnlTLT6bdd4MeHwGUAynJwEOPe20lMn4zA557hhMqv7jrYn0m8qe+QKTJS/c1SPsre7zXLc86Ym8Ngmcz/+AEQVUAH722bmvFvSkIv4uWCH3ANGjjRwReRRLhgcPabHB5gOIQB3Yv1OpiQihV8U+44ynKqEfxY+jlMWt3PP2HgvyHpFfaJEGHX95gWkQQR2dACfiqEcpUcfzasqKWVG8XIyVJeVoDZhJRTt1nWNLz9tvea26xHjqRTZZxfG54Dh2uS/McPwP5/u+sWx/DntD/ShBW8Y+najp+5Lz4J+2rrox1RhV5RFmZ/ip71eyf3APA//9l2C6KvcBzxxd42NYPlQLu4R8zMZ58d9yMQLwK5661P30lquHwF6CQ4uiWTlxUwv5wHL5kxHG6saZRlrge2ud0HziZd6TYNbp3EpcM2NcPQeGQTpOLVnLXd9j7JP72nbfdz16OzSfsf7+wD4U8j7m0utSowDQbgkKWUiRPDYPwqmTATY8EnYN4Jjb1lOSPUtwtpT8FXS95rDCHlo1kWlFLq+n1M/cySVtvUDHZeDNkzNV2jSkYZpAkOaQ4xIn913A9winCYZejml7ZpMG9lKy0uK2BGuWl7UVGu3SxrmvaP3VjnwpIKONa6uNK1h8k4223yMf1t1sNi3xKrd//zGHpZ3IT/aPU8DznAce6zHnLBvrFt6t7qHcCTtqGbtui6uKzAsdZN24WIFwP6p6WK6St77/N/x/0gBDFRpGK9qoIZmqZpmqbvLDeqKo++zKZQaVVVx9I1TdM0A+ql5Mvce3d7APhmoqaJJFFWN10AbvObPubm+tv85tv9uTn0v2n6t7ZNTYuqWejezPBGO69OKEuIIIjTQqHSKCme8O0uNUojhRP62sPVsqR6DWMJaWFJRaQ8fZxx9TO9yW1uMuTnX/M/T1UF22yOnDlFEMTBoBP46aF+UOpsxFfZbMMAAmHlO6XMMiJFSZTSNXknNr3klUmc2JWJ8Mp0zNIZv1RRVYV5a/i/fg147TdvHfhUyf38k6x7qEAAACAASURBVPbgu46l35JbawUgL18EHsWan/u9m5tM1KnDlmYw5SIcv0qd+BKYoYc2GnKHrGL64kMHPsSLxuUrQMzjMR1FzgfVlfx4yVzgWQmRLgQAcCx9s9RoVaTs7gP3kH8FPE849pcW31IsKxy+xEN5ZPjFQX9XX5PCbSKCyW2WDaaUGqtY163Z+IctVBqljr6zXJq1rI482jeRANkunizIhJkYjQHL3vib3F632qoLYbJNQ2SzAxhR94C/dIDH3jlOppBiFvfKGj9N///Ze78QN7I8z/fbAzZ02fRU0lPOyw3FtI7heoYioRZTncIekiYWlmIMHVs0srkktJox04xj50Gjh8oHq2DzocIPORe0ephSDr104xjIAafY69FAzfVLHQrtzSRyh2QKtGa2LlSIkc6D7Clc1dg1UPVQ9+Gc+KtQ6I+Vmcr070NBpXTihEKG/OY55/fnaxjgHKVGvQj4MRC3Ztp8yFpkRv28NnpI73mA8ccffripRnI6w6ydjM68fpLgEC8PReCPm/NLq6VGq1IQzbJplpuiUGk1SkZwRHtx2ag26sXkelW4tWTEfcUwwtHf/L0IjlLLtd6t4JZLF4H8H/zulwC3TbPmDn77JeD9/aPgWPQFgP+hDndNs/x33fTHFo8+GdGl/MUXACD220+QM1aDL8JWjRyetPeFGvXAShtBAUH+ZyUGT40C8h/Bz7a/CGD1TylzlSAWGc+x9q63Wq1WS4bM/ZSaALdm2t1SQ17SapQQu2bUdH+92Wq1Wo21tjMqfUgr1luxRamsFArqbmLN5zQ9H584sjpTNLcclDaKs+/dCYIgRpCue27NNCO6l/9vux7wmhKhbCEFd9prcrBeubXG4LX3D5s7XDm5SSP44XDIzPqZNUSWHQRxfFAE/gj5Yay7urR4x/957/3k0lErVurFijq4/dNKQfP7jP7gnXrrdtz9M+SN3wPA9NzQqMcdC9VWvVVRU7uPPpWbb6bnrl77KXvo4AfqmZo7BwCeh3M7I6o+tdv3W7cT78kHXr8B+B3rrr9fCd3e3ZrJVSl+tJ9dodJqVQC3th0r1Nd09GUbFdRMm18Y/c86jlNxdkvnr8SikmhiN9oWPlwWFq4b4Inm8crCKBCToDey37wzdbqapT5RK9arPdPujn9oWSmkbhlt+hHBizYGGXWbLQelRlFD8jRiSsh28ZRBJszE5Hhp1kETNt5N1b2cngP6ofbmruaeH54DMIGQRjPlNf0C0HE2PaPaKmrw9+9jTd0m1U999NC1ZMBdmiyNF900zv4vHQkO8dLQBv7oGG7mDFZqTB9Yll1Qlpbw7JlRatzS97dsxwMe73LA0LG/4wFGtVGJ9oSLNbn3PJQaLfXB0Yok2Qcvc+5oZDMVY70AqJ6pidPXnM6U4QgyR1X7E5srX7qXXDkTBPEyjN6xJ8g2WAvdIaNnjPns6TJXJ9r4PeqHPBK3ZjkejOpLdj0Ot+/EKwaZMBNTkEx9F9nW7DFG6F4/flO3Zh52AUwgpLGlVeFe1TBt7ncQcvf4BI0456KfqnPeVvOa6j86n95LZxQSHOLloQ388dLrC2jTaaS7xwFWKq+1Nx3uhGXiA3mUq2mFVhEQQrjufm+v1+Z8KFg/qpWJ+suQOTcd4Sv+fHo8qe37fBbOp+P8ks5fibNOWOoJo9qq5CZd5E7nvC5XtbHF7wgXyjH5nbR9JwjixIjpXvTgcjohle2S9txKoaCC95mLtOn0M1NaC5VWFaYtkxNYqdEobVltSqoniCOCNvBHRzKQJdyaZdsWpjPWkLHttWtXi8UGaltOsMVevqkUNz3DPkLGunXs3LQ5yg5OBcxfGr+n1Cu1cKbzV2IBmacPPGo2jwWVJm5HHMvF//S/exDpPvAAPiqb2x4gc1rjnkXdvQ/CTvOrBhs+GIj7wIv9tgfPi6XGOpb5qNT41SslTa8sZMJMTMC0PvCTE9O9fs9Tre/caYU0yLHP9doeK21krNJm00/1oKJZtnrrDb0bGZIdlNUd/2oFyK+TeI6ABId4SWgDfyQMOiLqAC9hRunmKnYnylAP/x5E2tmpWnm/+PyKyj23HA9ghrGmX9f1XK7Qf2Bm5C5F/9JMOxfC/fXdv3z47Bv5KpJOoOl5oPvbT2tliysfeOPtN4LC96xR0Wx7ABI1Zdw2ea7U+JCWzgRxvMzJBx4AWOgjqQp28tkT5NozWjb5xYuRFx9uFrc9XDQaO5W4j7F2bW3ZcfiBf4YqI1jLa7GDzKQPvFast3TZLcr3Re6O7BFNnFVGLaNpnU3MhOh1x180pHuJWdMJqdrBN/Wux9ZGGsHPpJ/hg/ZlEib6vbDoKdbw3r1r2p3lm2fcTOilmZeekEC9glAX+uPD485u6A2n6XnVVclHtvxIIq8L+rUDvoCrn3tdwKi26pVKsVgoFDRtor8XM81171p2sHtP+MDndAbv4XaQH+B5fPcgCP5nj75aBMbv313966jUJl4SxOkmpzN4zgPV0kK2zBiPVlw3wG1fWNza7mDElaL2fx1+g8t3diqqOWajxMD31Od9Nzwh8tYIH3jR606bwE+ceaRcj3pJEHE0PY9QiGQLo/GTpO6p5vIxtZxBSOXdHMeLbvwTzKif6kF94/inf2dzv0bzo7LNsXxTRvFFc6eDi757PHGEkEC9slAE/uhIBLLER3ff2+48x3J018r3lBe6bAKahsxJcraa+kaxoEG4wwLO99xbhYKGiN1coiN05KkSLqYTzxW1egc4f/VOffOG5jfV95wHbrFSALTXAADnr968u/nzqzj8m/K9Xe+b80uXpd191mix3rqmnFPD496JG2mdIsj4nTjVRFLoZSLPF2Fx5kXgjfjVqoN8JA/pEsMTKS5Z0wuV1s3Hpp+Rw65exmEfw4hH//QcwOfbprkdffsfDiuF5f32EzBjNSwGWDXYAU/4wP+iU948jN2z3wvachIEQcyA6HWBi772GNU7l/l2f7xsSt1Ts85fNZjHRWeAopYlpPjsMfCi86jsPEz4wF83wLmxrnKJhhdUs+tnTmeAvwTtdII7i68BnB/smuYu5DdvrO1Ii3hKYyKII4A28EdHqGRRLr4pj0RF738CAHcsLlPH2So7f+CFJZkBsieJ59jxxiXySu3aGnM8blvpu/9BRwDnewNV6m5UGz8JU+jHzI1z+Kv/9zmAbw63rZjg852PKoUbUryBbw53NyNf+ptnnwtc1caMIpaFRRDECXHlTWDw5pWMUZVCv7yi4dDb3ltvtSpQh24/1jV/JH8F8B2M/Cx00SxbjmqIPHo64NbM3YFaFarmziwlb3/Y2NKtmTb/oz++qopE89ffr8dNLbtqpurqLBA8qUT0umDYMU3l4ziB+9JIToWZJTEW8vsk4iRDIOlv/jjcMLs14Ltxspmqexd/V95wtJDiypsAP/z0Z41WXQ12udPeF0UdgHG9MNIHfg76KcMu8XOBP4odFLh7GG8hkgrp54SQQL3K0Ab++Fheujh49sbPbvlrwgvAEss98/oAM0oblcu/Kh5M2U8OgHbtrUuO9yT5dtTy+Jt/+q/OMwBgek6bcq6P+DzMnY9z4YeAyv9fvWk83VV58sy4+QbfPYj4wI8cVZmr6Ac18sDowtexLKb0k84Sp4RX1ge+3/PgGeutesG/ZbKxE0EQxPx94GPp59D0NwAPr+nAtD7wxXXDsduPfg3OSo1g/z43H/gM/ZxRdQmCmBHawB8J8qA18ebg2XNW+stYMtHra9X7df8NEYauYge6soiKlapBCr3teP7GGWL/0yfDXu6esgGVD/LsXMQHPnLn7LkxQi9SH3Vt9V4Bfq/8P/z3lfd/Xgkv6O4eRHzgR42q/b3nRLexB3bNpZUzQRw/r6wPfKEiA2MKuRbeUUVOU0JmkGcE8vskhpm7D3wi/RzMWAV/CmBaH3h3jwN4+HCl2ipqx+oDP1fol2tSSKBeYWgDf7zM4AP/0d9yAOyta3KTreXk4Sxekxv4SbzcF9wHXrbO808olN8e5zOunBdVtkhniVeMU+YDnw4FkF5pyO+TOC5i6eeiWVYb+OmEtFBpNfSy5bwhbzJvH/gM5qm6xKSQQL3K0Ab+6JiTD/zX3wCA99AyH4bvno+0jDrtPvBD0X2tUKka3OZnqvsJ6SzxajG1fXHAyA6caR9iJj4l/S7UX54giIXmBHzg566fpLoEcWzQBv748Pelk/jARzkH5K6unjtUBfKMGe+81d32d/NTe7lHmGGu2vEnFV/6wMcv7ffiPvAjR1MYcZhLEMRRk6iBlxjVaHb5pMzBBz5cyA4h6zZVTMrsrQcno/Iu7f9sOrLBByvdyavM/OSRpSe/bBDYurCCTkc9/LvvQjWCIl5h5Bkr2SwTAFRjYNkGeMybksX1gQ+sj1RFfxBykvMP/8p0lBRiZQXDbYZ/+6XfSznlO7g10/6fLGUWMW9IoF5ZaAN/JAw6IlgaBjCj9HbYm02ahQ6tVPORG6AzQPGqngf4sz9stN4PzjWbZcu/0vdyj6Re7XfHPpr8SzP1XPcD3zzZcyyzbVQ3/OJ55HQG/vGDD9pcnjMwtpr/NuYDP3JUlo+9e2ftq0cOV8MrF7K296cU0lnilJBeBu9OdY+czsB9m8mpfOAd2y7r8oTQrdl8RHco0dxyPFZqVApuzXQ8GNG76BeAzhN2p1W/Abh3TZtjRVaChuk+Itiwq95N1/IXHd5ZqbbuFaDU7qKRFb8ijpSFkspFeAbiNOD7wMs4jWphNHaS1L3addWFfiucNYOQyrs5Dlhp5P5dNG3HQ1DO79ZM2zZVfqhWfHvZ2e34Tsh/80tzd7B8M3444f7m4TMg2ow/8h1QszkAb4L6e+KlWCiRJI4Z2sAfHx6XenmyPvBJJp/r3lW79+D72FbXj8TLP1o8uMDzDjxgWXU/zRyVB7cPt8Mv5XkdACtnM/Q1rLMkwcQZJGFfzEqNat6yxwtTodKqwrRVl2ejVGJOO+UyGYdC2A1aJg6wUqNeRHOnA2aseoHJ8TLwIvWTOx2w0kZRAyD2u8+xstKxTVOOsUvwQBXwJ0Ci2ki+JHkkTgWFSqPUtQIf+Eapa6Up2NCsVhW+DzwrlQzP6QKYUUgLoQ98OmL/UR9AoqE+r9du7VQ00fzHAVZWwqZ6Kyvo/GNT/LwYWN6lLldj3wG4aMypypJIgUSSoA38cbP8ttJAqbARH3jDYDxVFNN94CVTebkDsf72U80Vv/6wM/yu51h39da9ApDTLwFJR7rv/B8yRzX9Qtonfjl5JexphSSYWCjUAmyEIBQqrWrNtGoodR3HAzzVZ9JfzjmW6YCVGq2WsiTe4azUWO9ZNgc8x9opNVoVeaJXrLeuNcu+43o4R5LTGSAP9HgbvsVxHHULy0GpsYEty8lHnJd7HmBEfIxFs2w5yZ4aUv+gtu/DvThEs2xN7+tJEMQZZqwPvPJH990o4dbsaBEQ03Ph/je1+hwA8G9d4GlPvVDVlwASQgoAyGO/bMrokGGAc5Qa9SLgl/+4NdPmQzlV2rV3mBMVTZUKqcP3Ain9ef1eNOvTF1DRLNuc+ap7JfHc8vSivVbKO05XH/NvSRDES0Ab+OOD5diXfe+17wvpfd7c4UM+8BxfhyVFAVqxvvlvv7y3O/gGAM4vG3/0GueeKj7SihvVf960DwbB5bkV1u+o3smDjgDO9waq8NOoNn4SKdbKnhtDPPrkWfrXevEFAIj99hPkjNVz3C/VXzW+PfD70GWPunudWJk/Y6V11ZCeIIgFgzvtUripdmum3Q1sKkWzbFk1PVgVeo61V5XmbG7NtIMhobbeLbkgjM6JDsmxMkascv0tt2gOj41vfuw+cDJSPFWG/qwZ9P/6r/8620TijX+ppr7/vcM/e/r7duoQQZwW0iVR7enVPlvU1i0OXFIb4GyNjQmyaHa5094/RFv5wCsj+FtDOjbkDBy2JRJAovlctMlRlupK3USpUdQfTGqrlwrpZzYkkgRoA39ELK9oOMzHzzylgTv76bWILE7kAw/RLG/uBpvsbwYyTq984CH2dyI7cAD9TujvJu/TvS+T7pme0+J3zpobQ7t9v3U78Z48aV6/Afjif/39Stjpyq2ZXBX5Z466exwwfrE5r2Sr0yL9JMHEopLoY5cI30QSM5VPUfA6aIDs9+kMl46F6wa4SvyM7Z1jfut+ZXsQFd8ota2gAHRCcjpDzMSi3/MAxA8mpe6k1en4AbLwMQiCIAJieec+EzbeTZXEnJ4D+qHw5q7mnh+eAzCBxkYFWXb/cDY9Q/rAq/37tXE6Jms41aNF2zFFv/R4R01/+65N2TCFIIjpoQ380ZHSzHmWFaGoved4wNK71Xu3CxrER7X3tvnzwAdeloIa1YbfT040y5YTa3XveQhOb2MWJBPMzXiu5pbjwVgvAKqXXiLmldOZqqdH9igApueE23ywI7vYMWash/3xCII4XtKb2Cmiv8lhGCfa3j0fXpxqIiTbZ4Z750Kl1VI/9ntefL05vFwdj+rhtNW8FumEl3iE5k56YEo9TgVwa6ZltkfmuGbze7/3e9NPIgAA/zJyhP5ViYUgmfousqzZE6RJothv9+M3dWvmYRfABBobW1oV7lUN0+a+urp7fIJGctKPyKjOJHWRbxFs318a+k0fA4kkQRv446bXF9Cmk7f+i+cA8OyhbT2Mvq984JW6CyFcd7+312tzPlS3OaqVySRz0xG+4r982Fz6pbS3LC/4bC/WH29aTo1+kQQTp59IP6PAi232u4leNzW8lZ/yPrFOeKzUaJS2rHZ0nTt0UJByj1slxocq54mj5rurf51oDhK8f/wPQxDHRWxnn9NZYL07ncZK3dpzK4WCCt5nLtLkqUDs8GCEi++YqqQ5bt+JcZBIEqAN/FGSjGIJt2bZtoWs4NYwovc00wd+2Nl4iAzdHTs3bY5b27K5B1aaX4tRz4skAqgPmDZv9rRBEkycetyazWORI5F9/Tg0PQ9g1rO7GCqOLnFrHvLryfz5MT6Vw1afBEEQR0OssXy/56nWd1NrbJC0lOu1x7TxkEcDqa30Yk8jet0R2VQBYr/twfNiZ6/O7ClMBEGMgzbwx4emWolOlw0KADi38if1zfeDl6JZDn6WoXAww1jTr+t6LlfoPxjZRjrBDHPVjj+p+NEeJz5hTxRkjaoleyRlXitUNkpdy5nYCo8giDnx2WOklf9kJNWztTCQLaty8mM+I+6UHPZtrhSGy9f9Dk6Zq8Dffgk8TvT/jBxOMmPVU4Go6JFl5FsGga38TYPv+u9eXp6kGR4xd+Qh5rBJBx1uEifNINIGOPtNiUwxHIPcc0fOChOzptNYtYNv6l2PrY00gg+9i1XKU6Dxcv7hX5mO7zy0sgKwtYQSxlVXK9ZbxSF/uQwjO+LlSBVJ4pWCNvBHwqAjAC+xCGZG6e0Xwavh6E60c0j49+CqnkdiRRsVcFlNGl1ci/3u2EeTf2mmnut+YCoreM+xzLZRDXfdOZ2Bf/zggzb3O8mv5r8NVr+Zo+e+Op/eE+Z86t/Ds0RCgml5SiwGyzdbv/r5JBfmdAYe5MrIvhgTILM8Vd86uA/Cfu+J8nUZfhpTxOn+5uEzIOrr5N417Q4uqS73tXWL4/Iddf96qxg5FHBrps1VLfy1tWXH+QcoSztRW7f488t3zrYELTLDOUpktEksPPHjSfeB403Q307qnl27LrvQSyGVs2bQWKWiDlhp5P5dNG1pPScPR92aadumyg/Vim8vO7sd/8/A3/zS3B0s34wr4bDq+h3zgvt155ikSUwEKeQrBW3gjw+PS72MRnT4nlrDBoehQ0i/eGerqW8UCxqEOyzgfM+9VShokNnnjodE/lMWk89176rde/B9IoXq8o8WDy7wvAMPWFZFppmjy79/AYffpDzahd9fTnn3zED7duLUoxXr1Z5p+2eVrNSo5i17vPpoxXoDZUsd28USegqVVkMPhsY01MNQyAcARO8FcB5P/GNB46bBdx81xQ2/k6cf3lLZqcoJvt8b4BLjln/wmruae374v1zcoEUoQRATIq3QlSYa1Uapa7UnmdWqwlSzWKlkeE4XwIwaK5eNGfFvsf+oDyDRcYTXa7d2Kppo/uMAKyuDXdPcBQCsrKDzj03xc3W7VNWdKNGeIIi5QRv442b5baWBUmG5Y3GpnswwGE/bw8tolefYaY1LtGtrzPG4bU2WNB8zqJtqrvj1h53hdz3Huqu37hWAnH4JeJIY/s7/IWtUu32/8YPhUvyLxr3bZ/QvAYWViIXlypvA4M0rGVfksV82w+CNqgwCAHiOtVNqtCrBLy7Tc+FiL7XUchxZKeyiWbY5KzU2sGU5+fCZZX1OZOMvmt3dSBqT372zWY5s31X4aGM+FZunxcxykSGjTWLxiBnxpr3pFwTVlfq4NVtVswOYWBL/rQs87akXmRobFWTDAOcoNepFwDf6cGumzYeOQbVr7zDHycdEsmw50KFyPFnpz+v3tOiYEtBRqit1V/kSRZ1FZoH0cxJIIV9xfuekH+AVguXYEvDa92ULEtHc4VhicnHKjFKj/pOvhCwpSqIV65s3l8+rV+eXDYMhuFIrblRXY39McitMWXaqhPmnvUGzbJqmadZcMegIQHQG4+fGEI8+eZb+tV58AQBiv/0EOWM1SBVjq0YOT9r7YvyojMdVDRbJM8uV/rJyRrfvBHGq4U57rdFqtVqtSKpkS9IowbFqoQew51h71+VQ1fDCIblYlNMSc6JDcqzcHNG2SSvW5UOkML52XZrRBzEqP3zk1kyfGpkZEwQxT9Il0a2Zptxnt1qtViP/33Y94DVdTsnW2KggV26tMXjt/cPmjl8YJM8lh/0ytWK9FdvUyxrOIH4ei6TLTkWRiWmqK3pdsO5OIJ8jdZsgiHlAEfgjYXlFw2E+fuYp3OaW7bCfRp2LXl+r3q8Hh5wrGg7VgWbslFc0y5u7A3/ONwMZp7/wQzVtf+dggAj9TujvJu/TvS/D20zPafE7Z82Nod2+37qdeE+eNK/fAPyY1/X3K0HfZ7g1k6si/+xR+QGFSr1QUcfFrDTWuXQ0C352S4emxMKTaGKXCN9EEjOVT1Hwesi1PVw6Fq4b4CrxU+6dq348fN1wbFUQL5pbjscCMyKtuFFqW9MaUgx1wuv3PADxfvKyEf31+G25bXZLfhF8s2yZtelMQwLICXIOkNEmsZikNu0ZW+iuLkuTxJyeA/qh8Oau5p4fngMwgcZGBVnTLwAdZ9Mzqi3ZRGSPj/PLlB9StoONfrQdU/RLjzHk6Pc8eMa6n3ogmmXL7JF+HiWkkK82tIE/OoY7OYPN4JIpau85HrD0bvXe7YIG8VHtvW3+HHhNbuBlR7vQg01uq2Ot7j0PspsTELcgmWBuxnM1t5wgYUr0ukMxr5zOVD09Mkej/yCiucOpcylBnCyZdefR32SVjI54e/d8eHFqRaRsnxnunSPplkP+7MPL1fEkOuG5tZQy+eZOamAqzKiPHywQxw8ZbRILSjL1XWRbs8dIk0Sx3+7Hb+rWzMMugAk0Nra0KtyrGqbNfXV198b2AIXvR2RUX658KGbcSfp5DJBCvuLQBv546fUFtOnUrP/iOQA8e2hbD6PvvyH/55dzCuG6+729XpvzoR6lozbEk8xNR/iKP98mo+4Dx0tN95qGRT96pENT4gwR6WcUeLHNfjfR66aGt/JT3kd1hJL3YaVGo7RltaPr3KGDAknUrkkyLuxEEAQxB2I7+5zOAuvd6TRWNk3acyuFggreZ66n5KlA7PAgp6cmE8zkqEn6SRBHBW3gj45kFEu4Ncu2LUyXUyR6T4Hc1dVzhwfK05gZ77zV3Q4389Fj2VQydHfs3LQ5bm3L5h7Y3D1CZFLrGQ+/06EpcXZQbdzDxd9LVj3KWstZmt0NEYsHuTUP+fVk/nwsFKbpeQRLZmIxIKNN4pUhlpDY73mq9d3UGhskLeV67cCfMx15NJDaSi/2NNRfflEhhXyVoQ388aGpVqLTZYMCAM6t/El98/3gpWiWg59lKBzMMNb067qeyxX6D1ItPlKYYa7a8ScVP23xKwvfdQ1A9mjwOGlFqQRBHCOfPUZa+U9GUn00ai2rcvJjPiPulBz2ba4UhsvXI57to2/42y9lV8/YgWl4OMmMVU8FoqJHlpFvaVRblXNfnU8L/j8d82WIIyd1VUprVmJxefp3pmkb1ValEHhWZiH33L0+AKmFDT02azqNVTv4pt71WP4Nyyyny6ese1cxfbO3Hii8nN/+z6YjnYNY6U7eA1vLDYd8PKmhQWLAhRV0lF0Re/dd0ILueJiLBpKinjpoA38kDDoikLYAZpTefhG8kmvYaHpRtHPIoCMAdAYoXtXzSKxoowIuq0mji2ux3x37aOgMUNSmnut+YCoreM+xzLZR3fCL5+W6++MHH7S5TBRgbDX/bRD8zx4FAOE2/6rOIbtIGaWNylkOw9OhKbHYLN9s/ernk1yY0xl40GNO9sWYAJnl6ZdHug+cIFCUKF+X4acxRZzubx4+A6J2Gu5d0+7gkmz9IWrrFsflO+r+9VYxciigmmbeKgDYrJeCgwQAH/2Fuf350rt/TgvQBYNsOIkFRi7t/HM/94HjTdDfTuqeXbve0AG82NsKZ82gsUpFHbDSOxc8nnoG6bcLrRTcmul4MGJf4QLQecLutOo3pJpyrEgRDgry5V7+wgo6HaWf1/IXHd5ZqbbuFaBWixeNrPg/sSCQop5SyEbu+PC4s3uQSGjne03fVa48IvRduG4AnrPVdKUjmzss4HzPVVlVwq3J0W5vwlzWyee6d9XuPfg+dmjwpOl5oK/25wA874D3sazOjbNHAbdm2U7neXBjx3oFHJwCfUzNqCeIU4BWrFcNcFv6BlnttUbVmER9tGK9UYJjmaZpJgLshUorHEpzMI7h1kwzrksARO8FcB5P5E0s/uObBj5/FJoaBUExlZ3qDHXXRwAAIABJREFU963TivVWFerLmNufI1e6d/sMnyQSBDF3CpVGaXkAgNumuXe9UZqoPX2h0qoa3LYcDzh4vFYKNtQzaWzhugHAWC/+cMQFMgzkOZapki7lB5SbAhDNnQ6YseptSw3uYBl4kfqBnU6gn2K/+xwrKx31oPYBu4TnGLYkJghiPlAE/rhZflstFgvXDXDOHYvLlE1mGIyn7eFltMpz7LTGJdq1NeZ43LYmS5qPGdRNNVf8+sPO8LueY93VW/cKQE6/BDxJDH/n/5A5KptEX1rGk4HMOXObW7Zj167PZkByKqAjT2IxufImMHjzSsYVeeyXTceD6sWsKoMAAJ5j7ZQarUqw6WV6LuzAlFpqOY6szkmiWbY5KzU2sGU5+fCZZX1OZOMvmt3dSBqT372zWY5u3yWydF6Z0c/uZrngZpanF7LhJE6UsCn86De14p8aTnj2WKj2TKuGUtdxPMALHDNl0MaxTEdKY6HSat1qli0n/6dFfc9BXu/XzHB5FuqnKDGrvS/0nqXuUEZUWgPJ5TvNiIH70BNfkyqn9DPUS1fZ/tb9HiKiWbacWBoo/PUjYsef0X8Y0SxbU/ZXCiH9PDZIUU8vFIE/PliOLQGvfd8Pue9wLDG5OGVGqVH/yVdCFnIm0Yr1zZvL59Wr88uGwRBcqRU3qqvR7FHkVpjq/akS5p/2Bs2yaZqmWXPFoCMA0RmMnxtDPPrkWfrXevEFAIj99hPkjNXgrJmtGjk8ae+LsaPSgu7d66/5i3WtUNwoMfC9Mx+EJ4jTB3faa41Wq9Vq1Yuan43ekjRKcKxI9oznWHvX5VDV8MIhtUFupMyJDsmxcnNEsEkr1uVDpDC+ZbI0o0/tmZkxRBAEMTVx2RxWOdOMpB1+VrM5Vp7aM0irWzNtvmowsFKjsdZ2MsIzR6afCDL0X85RiCCIDCgCfyQsr2g4zMdTP2Vcmf00alP0+lr1fj0obV/RcKjCSJE4OSCa5c3dgT/nm4GM019QuVFif+dggAj9TujvJu/TvS+T7pme0+J3zpobQ7t9v3U78Z5sPbV+A/BjXtffr4Q+oG7N5KrIP3sUAPCDd+rJDxhyiZ+MxT+7pSNPYrFJNLFLZLFHFm3Kpyh4PeTaHi7hCtcNcPU7Ldd+VT8eHvEL9iszg6jORqltBQWgEzLUCa/f8wDEDY0yembOoZ0mWUIeFWTDSZxKYrKZpnJPA9n1drulzVJ705laWkWtzgEc8Hy1VdSAerVn2t0pn/Ql9dNPAAi/4PTQ7/LxQYp6aqEN/NEx3Ml5JkUTtfccD1h6t3rvdkGD+Kj23jZ/DrwmN/CylMmoNvx+cqJZtpxYq3vPg+zmBMQtSCaYm/FczS3Hg7FeAPwoevzMNqcztQlH5qjs+eJsNfWNYkFDWOY/UeUYQRDzJrPuPPqbHKZNRtsT58OLU62HZPvMcO1XqLRa6schf/ahdesEJDrhyRqdxCM0dzjSA0QZQ8SJQzacxKkkKpsjVC7v93LPV+vFqyhOL61iv/scQ57u3Skf9eX00zfwdGumZbbnYglKHCGkqKcX2sAfL72+gDadnPVfPAeAZw9t62H0/Tfk//xyTiFcd7+312tzPlR3NCrNaZK56QjpQJcarJ+azCL/6TkFp4Z05EmcFcIS99BLaPa7iV4XaV5u+SnvU6i0qjBteR9WajRKW1Y7eoQ4tISeaIggCOIlmVDlZpXWOfi1v4x+Bve4VWJ8qHKeIIh5QRv4oyMZxRJuzbJtC5lNlYcQvadA7urquUPVw50x4523utvhZj7pzDlERjXT2Llpc9zals09sFJjTk3mtGK9ocubAmBGdeNWb2s+u/mFhI48iYVlOh941cY9jLJMaH4xCk3PA5g6apPmA5/TGeABgNfe/zTvIb+uJRQvXEKHX8+9G3SWyk5EIE4KsuEkFp3PHmNYknyyVO6wI4CvPwMws7TGag8//e8exNIAmYI6T/0MTx2wsgJ04on3xCJCinpKoQ388aGpds3TZYMCAM6t/El98/3gpWiWg59lKBzMMNb067qeyxX6D8wRjnRJZpir9DvZT1rT80gmasnCd10DkD0KAFqhUm9FKuT3YqMEQRwjE/vAAwBbCwMxsionP2aGrJoJhVB206i2KoXh8suIZ/uo2w37wEPZu8vKIbmmXKkW4GcdhR8Y9YGXV3YuAj/2h8wpz1uJIyW6xKRVJnFayVC52HVTS6tMxY/umb94MfZp5qSfcm14qdRotTQAovmfLKeP5bHN8IgFIUNRaW+/mNAG/kgYdATgJaJYzCi9HYqpXMNGlbbfC/V50BEAOgMUr+p5JLQ+quSymjS6yBT73bGPhs4ARW3que4HvuWy51hm26hu+MXz8i/Sxw8+aCuzd8ZW898Gwf/Y3yu3Ztq4U+rGUwP8uD4AxlYueJO0QT3F0JEncRbI6Qw86DEn+2JMgMyuVH3r4D5wPFbaKABD5ZcyxG+MtnOLBnwCZCcn9o7yXJKu719G4lL9nge2lkv4wIvmDscl9vwJ9NzQQxInCvluEmeHDJULmgrPJq3yznZZb6jy9d0BcH709XPUT+217wG48Jq6RtP/d6CPH1EY5lRDwrvI0Ab++PC49E6O7kv5nloe+p6gw0i/+KDHm9/hLQrfc28VVP+3mhyduIP75HPdu2r3Hnwf2woiY/I8ggcXeN6BByyrIqmwT90vsGNzYPn/dgZBCVUyj9/zOgCW185+FWpCB2k/Tyw4nz0GXnQelZ2Hvg98vdoz7chZ5SWGJ1JB5FnhF+ES8WLQuQNasb7ZKW6qFMzzVzebwSa5UGltfhUMAezOyBi47wP/i0558zDyfh/PgYvetmluAwCMOyVsR6ox1ZJ0IBsxxX3gv/0SeH3mfx+CIIgEw7JZHKtyWnE2aS1UWjcfm352O7t6GYfDtsCS+eqn+78+BzPeCGaBhb2TCYKYP7SBP26W31aSJjfm3LG4lHBmGIyn7eEze7xp19aY43HbmixpPmZQN9Vc8esPO8Pveo51V2/dKwA5/RLwJDH8XfAdZPWAY2/K14NBpLWednnpPLxvEnPPTfiFzgR0zEksCFfeBAZvXskY5Yef/qzR8u0v3QfRSk3RLFu+99HyioZDb3tvvdWqQMV6fqwHfhebh5qcJppla7Pm+gvY6BBEs2xtl5tvpSfQ+z3wBUIHTh/2s0jWfbToKJjn1kwvFtyX0StuVO/LsNgDZ/ZudotvZnmKIN9N4pQRl9Fh2Rypcsuhm7C7N4u0ujVzd6ASK6XbPNjKcvL5AMxZP0WvC+Svv78O/1TBe4nyd9LPRYCEd8H5nZN+gFcIlmNLwGvfl71IRHOHY4nJaDwzSo36T74SspNIEq1Y37y57KdBnV82DIbgSq24UV2NyXNuhSntVAnzT3uDZtk0TdOsuWLQEYDoDMbPjSEeffIs/Wu9+AIAxH77CXLGamD9xlaNHJ6099M7r1xcqYYd8MTnz77BJeYnJjBm3HmXoT9qLkEQRwu3zSg1NzY65AMfvtaurTHwvfD6uFkxuj0BqL2xP00rrhvgO00hb7jleGFURytulJjnPIg/wDhyOoMX1Y9+zxvStTQf40Kl1Sh11Ze3u6VGizyQCIKYA8M+8JkqN5u0ylm+O5BWrFeN6Z90Zv0Et80dvdFqtVotqaS16ZSbIIjJoQj8kSBPUePtj4Tb3LId9tNoQOf1ter9eiDq4dFrNE4uD2t3g9KobwYyTn/hh2ra/s7BABH6ndDfTd6ne19mqDM9p8XvnDU3hnb7fut24j3ZyWT9BuD3pLv+fiXSiK5m8kiRv6785uVp8X+M5OjLuesf/tdCZO72Qz7j+e2pO7ulY05iwXhFfeCD1k11TX0j05mxEz35Qc4T8t0kTjsT+MDvuZVCcNFM0ir227I6PeCYfeAjFUnyLjP2EKHf64WAhHexoQ380TFsxQRWakytZaL2nuMBS+9W790uaBAf1d7b5s+B1+QGXna0U5tjQKl9rNW95yEsRopGtSeYm/FczS3Hg7FeANSKPNF1LqezSD19oVhJu8tEcwmCWFDOjg+8SJTEqyJU6mJ34pDvJnGWOLs+8Gyoc9HL5NETJwwJ74JDG/jjpdcX0KZTs/6L5wDw7KFtPYy+r3qWqHNaIYTr7vf2em3Oh5qVRvKwYkwyNx0hHehSg/Unzek7GqRjTuKUsiA+8GkUKq3QmNKtKR9j//UeH/KpTDhbAqoTNK1ACYKYH1kqFwjo7NI6n8jHtPqZZiVMEMQRQhv4oyOZeyncmmXb1pTGwqL3FMhdXT13KP3ZwJjxzlvd7XAzn2ziPkSGH9vYuWlzlOEbKzUWcPt+CqFjTmJx+Owx0rKHMjLJT9wHHvjtl7IpSExvQ21jxqoHo1qIvxv9lka1VcnpDPj//uE/mbas9WSlxnovkZBKnBDku0mcJj57jGFJCshQuUFHAF9/BlzBXHzgP37k4dnSAJknonPRz3NfnU9LK3ia/cTEYkPCu8hQE7vjQytUqgZibUgm5dzKn9Rbinq98tYPwiEZCgczjFKpWq02Gq3Ju5bMMFc0y5bNPZZo7ySPlONIX/vx2VwvM5cgiPmzfLOVYMTuPaczhN2XpvKBD/rWKR/4W4EPPDxnSw0ph+QRKUQK9zcPEx023bumaTkvSo1Wq9VqGE/5AS7/gbp/vdVqVQ1pciR/UrWcWvHtZfQPn1zdbLVarVb1Dcca/9nEUfO9wz+T/wH47upfy/9O+qEI4iWYROVmk1bZEdQu+9Ja2x2MmTE3/by6WS8xwKiqPxl3LgNYevfPKcpz+hkrvFGVJo4NisAfCYOOALxEFIsZpbdfBK9kECp6VCo3rZEboDNA8aqeR+KwNnoUK9tBRaNjYr879tHQGaCoTT3X/cBUVvCeY5lto7rhF8/LI+WPH3zQ5jJRgLHV/Lfpwf+nSJz2Jo6j3Zpp84uZiQNnDjrmJE4lCbNiVmpU85Y9PodTK9YbKFu+WXE0wF6otBp6MDSmoV60TjRE9F4A5/HEDwcZNw2++6gpbviNQKSPcZCmKsveRfMfB8hdvXS4aZpy+CLwxiukQosGmWsSZ5XxKjertMbK12EYl8FH+cBjrvopd/d6zfTlE8iV7t2m48+zDan0CUIR+OPD487uQWJfyveavqtceVhHASiLEM/ZarrSZskdPorle646yRVuTY4qu6bxTD7Xvat278H3sS3/oFeeR/TV7h2A5x3wPpaHWpqI5q+G7OQ1PR9+Q9Xx9HlKO5RTzSQnlBRfIk6cK28CGOkDDwB57EtXSrPcFIBMLJJ4jrWjR5NzmJ5za2ZoyzZ9bXvWOZ5olm3OSo1GicWeud/zgD+qhukD/z7ui6QV661WvYh417p+zwP+jz/+MJjW+FnCTYkgCGIChmU0IZtxQpVbXtGCiS8vrUzXfwRoI3zg56ufUM8sLeQYwEpVyl4iiKODIvDHzfLbStMK1w1wzh2LyzNYZhiMp+3hC7dKjDueY6e1INWurTHH47aVvvtP+fzQRm6queLXHw7tvAHPse7qrXsFIKdfAp4khr+L38Ktbdk8eQ3k3ylu89g3XDkzmat0QkmcKbjT9p3WEBRvKpcL0SxbVk0PokmeY+1VZTMkt2bawZBoli1HmWMk5kSH5FgZIzb+vtOSaA6PjU/gkWb08TXm3NJ+Tp2Z5aJB5prEmSIum5Oo3GzS6tZMm6uAvvwUsBGPNH/9HD80IaSfpwJS6ZOFIvDHB8uxJeC17/sh9x2OJSZFkhmlRv0nXwmZW55EK9Y3by6fV6/OLxsGQ3ClVtyorsbOV3MrTJl3qIT5p71BU5791lwx6AhAdAbj58YQjz55NvQmAODFFwAg9ttPkDNWgz8VbNXI4Ul4aOvWTMvmMKpG2mFwodKoGtE/GKv/kSqnCOKE4LYZpRZv2xEp1hTNnVjxpnZtjcXafIRWwYXrRpDdIxd4apos21QF8aK55XjRpMyNEgsrQSckpydC5/2eN6RrspFyaEY/2SyCIIgZiMnmeJWbTVrlLN8dSCvWJ2+JFDKbfk40RBDE3KAI/JGwvKLhMB+vahJuc8t22E+jieGvr1Xv1wNRX9FwmL8S3gAq8Uk0y5thK5JvBjJOf+GHatr+zkGsT0m/E/q7yft078uke6bntPids+bG0G7fb91OvCd7R6/fAHwLpuvvV0K3d7dm8kiRv6785t0a+GA4RVcD+rISDDXT5heGnmBiFurslk4oiVNIZt15NDbjx3Di7Ynz4cWpnShl941wgVeotFrqxyF/YdlXOexYPwlacd1wbGerea1e1IKynPgjyPTPW4WpZk0OGUC+LGSuSZwlorKZoXLBRTNJq9hvJ3wzcjqb2txtNv2cYGhy6Hf8dEAqfaLQBv7oGLZiAis1ps4qErX3HA9Yerd673ZBg/io9t42fw68JjfwsqOd2hwDSu15dMHreQjysGJmohPMzXiu5pbjwVgvAGpFnki6yuksYklaKFbS7uLj1myufOmmb9JPEMQJEemCZFRblZxM2pwV0esizYsoP+V9Yp2cWKnRKG1Z7ag+DS2hJ5pFHBtkrkmcVSZUuVmldQ72PbPp59gh4oxBKn2y0Ab+eOn1BbTplK3/4jkAPHtoWw+j778h/6fOaYUQrrvf2+u1OR9yGxnlhDTJ3HSEdKBLDdbPgNq+z6fqfbFO/uiEkjjDqDbEYfXmS3Z8k56SszS7G6JQkQWiAAC35iG/Ht5TJnmmLHQzZxEEQbwsWSoXCOjs0jq+U/0kzKafmUMEQcwT2sAfHck0VOHWLNu2kOmKNIToPQVyV1fPHaoO74wZ77zV3Q4389EEq1Qy2pGMnZs2x61t2dyDCpi/NH4r1LPStC4GnVASp4vPHiMteygjqT7qFxG1uByN9NAMM31kMU61VSkkPSURtHLK3NL/9suENSVi2saMVQ9GNRz96G958C0T3yzqq7SM2CziKBk20SRzTeK08tljDEtSQIbKDToC+Poz4ApmkFaZih91J/74kYdnSwNkLq8m18/oijHyVyIU0QxpJc4iiSUuqfRxQhv440NTzdanK+cEAJxb+ZP65vvBS9EsBz/LUDiYYazp13U9lyv0H6Q4e6Yyw1yl3yyxoNb0PJKVVrIsfuxJrPy7hEQ+GbdNnvwQgiCOheWbrV/9fJILczoDdx64RdVbfsjiMh3prLHTvFUoarKlHSttFICh8ksZhxrTz9j9zcNnQLQ5pnvXtDu4pLrc19Ytjst3fNF1a+b25775vFszbVMdqqq9u1x2iuYvLWeAH03ydYiXItung1aExFkjQ+WCnkSzSau8s13WG6p8fXcAnM+eM41+yqzN8FDVrZk2DwreR0orcTYZjk597/DPSLGPDdrAHwmDjgC8RBSLGaW3XwSvZBAqelTa74UHrIOOANAZoHhVzyNxWBs9ipXtoKIiKfa7Yx8NnQGK2tRz3Q9MZQXvOZbZNqobfvG8/Gvz8YMP2soKnrHV/Lfpwf+nyDiajvLtuAtOBxRHIs4sWrFe7Zm2L3Ws1KjmLXt8DqdWrDdQttSpXeyorlBpNfRgaFwYJxowDxC9F8B5PPFPBY2bBt991BQ3ipqy/1iCMtWIHiXk9BzQDwJLRrX6pm3vNEWBjhEJgpgj41VuVmmNla/DMC6DZ/hoTK2fcrzrz7YjTvAZ0pr9j0EQxCzQBv748LjjIbGn5XtK3kSzPCL0Lf3ina2mvlEsaBDu8FEs33NvFQoaZHq742GKMqjJ57p31e49+D62FaS2yvMIHlzgeQcesDzUykQ0f5Wwk9eK9VYxJZWflV7GRnThmHzfTlt94gS58iaQ4hIRIY/9sim1rNSoF1ViEQDAc6ydUqNVCX5xmZ4Ll4gzpdRk+RH71Tcb2LKcfPjMMvknsiQWze5u9BT09Z827g89idhv9+PPKJrJTNeJWSgvjEWGfDqIM8iwjCZkM64oocotR8yIXl5ama7/CPhuJc27d3b99JsnNcuR7bsiVVqnh/Rz8SHpPnFoA3/cLL+t1E1uzLljcXkGywyD8bQ9vDzI9Bw7rQWpdm2NOR63rUkdjyI2clPNFb/+sDP8rudYd/XWvQKQ0y8BTxLD38Vv4da2bJ68BlA5Bf4fDLdm2tyovoLZ89mppASxEHCnXWq0fPtLlU3Z8oMwZcuq6cHSz3OsvapshuTWTDsYEs2y5ShzjMSc6JAcK2PExt93WhLN4bFRG3+VveqnproPHL9nsgASHZzTKoMIgiCmJi6bk6jcbNKq1k+tSsH/FLARjzSLfkYe74HjxaqbRksrQRBHAG3gjw+WY1/2vde+LwDNTzdiuWdeH2BGaaNy+VdFjq9Tcsu1Yn3z3355b3fwDQCcXzb+6DXOPZWFrhU3qv+8aUfs3HMrrN/xZHL+oCOA872BCnAb1cZPwhT6MXNjiEefPEv/Wi++AACx336CnLF6jvu99laNbw8ih7bysJgZVeOxzQeJe/R7CfNSgiBOjkQTu0R+Z8TVQjR3YiXqQ67toR9w4boBrrJ7Yms/ue5TqZaiueV4YUtLrbhRalvBmnBChnpE9XseAKVrKnvVNNUD+ivkaA1ThDQ5HA95TEwK+XQQrwQx2RyvcrNJq5ql7qPS8LtTPmm2fipkt/nrMVkeJa3TQ7/7pwCS7pOGNvBHgkyDiq96hdvcsh320+iB5Otr1fv1QNQjuVORODkgmuXN3WDP+81Axukv/FBN2985iG2I+53Q303ep3tfpqczPafF75w1N4Z2+37rduI92Tt6/QbgJ11df78Sur27NZNHivx15Tfv1sATKbqi1wXy6NfKFvcAMCP40zQDpzT5ivKRiIUhs+48GpvxYzjx9sT58OJUT2LZfSNc+xUqrZb6cchEeGjdOgGJHlFuLVqepNouqWCYaJYt06F2yScG+XQQrwRR2cxQueCimaRV7LcTsZCczqZOIsrUT/VBzR0eOUKQkLS+UpB0nzi0gT86hq2YMItZmqi953jA0rvVe7cLGsRHtfe2+XPgNbmBlx3t1OYYUGofa3XveQiPQqNmohPMzXiu5pbjwVgvAGpFnki6yuksUk9fKFbS7gJA7f49J9iletyxeI+EnyAWnkgXJKPaquRk0uasiF5XtshM3CM/5X1inZxYqdEobVltPRcsPIO6TRWj2mneKlzTU1NNx+eSEgRBTMyEKjertKYemk7HSP30GTqCyJTWV68ckiCOAdrAHy+9voA2nZj1XzwHgGcPbeth9P035P/8fiJCuO5+b6/X5nzIbSSSuxVjkrnpCOlAlxqsnxrZ05SVqhtFv5ueZXM+q/Cf1uwdykciFoDpfOBVG+KwelMMXzMNmp4HMHWzuzQf45zOAA8AvPb+p3kP+XUNcHseYPzbAzO0ML5TYuC9Pq4BwYmjaJat3npD785lOUxkQj4dxFkj0wc+S+UOfR94zCytsS7En/53D2IWH/h0/Yy1G3ZiTfTR84DV3pZpqirKUmNDZ3GrJeJsQdJ9stAG/uhILnmFW7Ns25rSGFP0ngK5q6vnDv3icma881Z3O9zMp3Rwj5MRQho7N22OW9uyuQdWaswnRB7miqnXqvnqbO2fTyuUj0QsDBP7wAMAWwsDMVGLy9FIz4ow00cW41RblcJw+WXEc3jU7YZ9jMNkTpkCatocK9UC1LKU7/Joy0wHYGs5aLk15ji9PtCXx5PUm+M4OVKhoyUmsUBkqFzsuqmlVabiR7fMX7zInABMp59ysRYKdsQHXujLwAH/NpxlWcsgAT1NzKaTJKonxe+c9AO8QmiFStUA+J479dRzK39Sbynq9cpbPwiHZCgczDBKpWq12mi0qsakt51hrmiWLZt7rNRoRVfU8kg5juwJNVv4KpeezUoQxCKR0xk854GSNDFscZlO4VaJge80ZVDJfeB4fjmlVlw34DlbakiG+EelEAFwa6YZt7cEIGp1DrB3VPtm6Vr8ZU8A0K7lLwJY+l21qsz97hKAi/lrmvpsbpsqcfXp32V/NHEa+N7hn0UPRhMvCeIEmETlZpNWpWFlX1pru8mOwXGm1U9Jv+eB6bmED3zcdahwy7iIQXAXYrEhnTyNUAT+RBBu88H/8xR47limA8aM9Y1KIb0JsgxWxQ5ro0exsh1UNKgv9ruJW3zb+U259x/q8pJB2IV+krkx3A98rfccy2wb1Q2/eF4eKX/84IO26kLP2Gr+23jw34/cA8D/+DtXFKKF95F8Mrdm2nwZgPGq5a5SPhKx4Hz2GHjReVR2HvqGxvVqz7QjGfeXGJ7IHE4pNV+ElZwXg8IfaMX6Zqe4qVIwz1/dbAbyVqi0Nr8KhgB2Z2TKku9j/ItOefMw8n4fz4GL3rZpbgMAjDslbDtKRPU3gHN4aJkqiWlpCXhdlz+HaaMAOh1qwEQQxEszLJvFsSqnSsinltZCpXXzselnt7Orl3HYH/FYM+kngi39IF703u89AVaNc2Fi/dISgK8FZdATxJFAG/jjQ8hmnrmv7ptWTFE9j9vWP11Euk+b9It3tpr6RrGgQbjDR7F8z71V8MvH5Wi0DOqFd/Ds3/2HEU81Zm4E9278pNbjthWktspzBh5c4HkHHrAcdjlxa1aklenzjm3V1OpYZn05WzV9o1LQVMfTwZBDySvD8L6dtvTEsXHlTSDhEpEY5Yef/iziA/8gWqkpmmXLUd5HyysaDr3tvfXQrJj/WA9O7TYPNTlNNMvWZs2N+MAHQxDNsrVdbr41xgceoYGHD/tZJOteNMvhnHXDsWMp9DzwBgmNkcuWk3+ZDh+n1AvjjEHWHsTJEJfRYdkcqXLLoRmRuzeLtLo1c3eQ8IFfWU4+H4DZ9DOc59bMuA88APaHt+rvV8JZMzc0Jf08TkgnTym0gT86UrrQA1juH/YBZlQ3/mDP2g4vePYcy8tIS3cq3Cox7niOnSaG2rU15njctlI+KpWIjdxUc8WvP+wMv+s51l29da8A5PRLwJPEcJBSJZo7XPWpwwNHChi/AAAgAElEQVTT5uwSvKBLnVpSxx9k+WWc5M4MiSwm+ZK28cQR80r6wBMEQRwVZ90HfqJZBEHMDdrAHx+MGW+91X340PMPVQuNr7HlcA9gq1e/PTjsqxC8aJYt5ylw7u8t0wGMaqPe0MP0c4XqTaIVN6q95Bjg/XMf0GTC/DfP5IqclRr1YiSFHlpxo/rPm3bECj63wvodL0VyxaNP0hME8OILABD77SfIGavnuN9rb9X49sCXc+lOWtooFjS4AJD/WSwnq1BpNfSa7fA+AFwEnq/+KS2qCeKEIB/4l4WcIxYCsvYgFoQz7wM/wazJoV/PY4V08nRCG/gjIdlXXSGaZSt6EKsVK3XfHl0IoWkRq/Y3GPM8DwDTcxq04Sqmg/tNIQPYhVtrjHuJHfzBnotCQaZaRd+PROABsb9zEAv69zsjvOG02/dbt1O+jpNfvwGow1bj+vuV0O3drZlcWYjIUfn3plBptSqAW9uOGYxoOvrKjaRm2vzC8L/epJyZ5CvKayIWn7PjA09HhmcOsvYgFpCz6QM/wSxiMSGdPKXQBv44Ubvc1FCPlrCH9zzI3E7RLJumdxEAjGqjUgiqyfv+WadsamdUG5VoWzjp0lSst66FHk2Im4lmTRz7ZWRbVGNd3rXXHbKry+lM1dMjc1QD5HGt8qWbvkk/QRAnwoL4wKehTgolbi3mAx9b4ebIrJggiGMjS+UCAZ1dWkc0MZqSdP30X+/x9C7DmbMIgpgntIFfWPxAvXZtLed4fQAIq8RZ6U7e2VYZ6ireL4Rw3f3eXq/Nkwn1I5h5IoR0oEsN1s+A2r7PJwR2dnJ+KK+JOHY+e4y0/h0ZSeYn7gMP/PZL4PFnQPQJI4mnzFj1YIQ+8N29D0LvJOOmEck6jUS9nu7NYx1MnCxk7UGcAJ89xrAkBWSo3KAjgK8/A65gLj7wHz/y8GxpgMzF1eT6Kfnobzn8vxKJvwwRAcUyYrOIBYZ08jRCG/hFJQxYa8Xqu39vPfRL0JXpnOb2tnlXvRctkpqKWSb6dnAqYP7S+F4mlMGagPKaiBNi+WbrVz+f5MKczsCD7ktT+cBzx89blz7wG4EPfKSQUjkkVzPFwf3Nw2dAtNGye9e0O7ikcphq6xbH5Tv+/Vccmx9cvtP6LzcAfPQX5jbHSlV9mCn70+eaZcv5mlsmyEhukZh5fUmaSSwQGSoXlDTOJq3yznZZb6hC9N0BcD57zjT6CcCtmdufKzM8za2Ztq+Sau8ud/Si+UvLGeBHU/7LEMdFqpaSTp4uaAN/nOR0Fgs8RRHN2lZPX79VTIv5/OB14N+NXErKaDiYYazp13U9lyv0H5iTNBCZYaLa8bNEREzT80i2SokUvmeNimbbA5AoCAua7tGuniAWloRZMSs1qnnLHp/DqRXrDZQt36w4+puumsQHepDdYC4a8AkQvRfAeTzxRcW4afDdR01xo6jJsaWlzwOT46UlPHvREyggbOAsAOBHBhtMWE9EHDXkx0GcJcar3KzSGitEh2FcBh/lA49Z9LO5w7G05JseR49ic3oO6AfpW0a1+qZt76hWTcTCQFp6ZqAN/HGifNt2mj8BPtlp+43nGDPW34HDPRiojJycWEq6exxggN/SOar/Yr8bnRnLj4p0oR87MYkbJJ56jmW2jeqGXzwvj4s/fvBBW3WhZ2w1/22QR5A9OopvswZfDSiviThmsn3gASCP/bLpeFAb70KlanC1CPQca6fUaFWCBRvTc+EScaYTuSyR8JN3NrBlOREfY3k6GFE20ezuRlNWX/9p434x0vcDQKz1qFast4qiWT4AQRDEtAzLaEI24zoYqlzEBx4vL61M138EfDfCB34e+hm52367H38Q0UxWChAEMT9oA3+sKMdzZzPWQ9zzuL0NJEw5otOUYXvtumo3J5plm/sVUgIA+J57qyDH3JpMtvLPagf/8gL49l9FepOmjIkJ3Lt2bD3rcdsKalNlVSsPLvC8Aw9YVm1KM0fVWjmZys9K2XmzZ4rsLTrt24kFgjtt34QNQfGm8lIXzbJl1fRg6ec51l5VtjVya6YdDMltc9CmMzonOiTHyhix8fe33KI5PDZq46+yV/3UVPeBk9JOGYFV88aM4fcz44WxCJAfB3HqicvmJCo3m7QGdUCF4HSSjXik+eqnABId8NMSMyeE9POIIC09S9AGfoEY7f6hFdeNts3DJnYAAi8kf3tvZeW+P+eW2S016sWIjdxEE33Erz/sDL/rOdZdvXWvAOT0S8CTxPB3/g/Zo7I/i3/iq/4AvSLZ85TORCweiSZ2ifzO0AhTJlRGStSHXNvDU8nCdQNcHQ66DxwvmCaXhKogXu2b/Y4YWnGj1LaC5eKEDPWI6vc8AKq3k8peNU31gP4KOcAPbFFjDoIg5kRMNser3GzSKsI6IARp+N0pn3Q2/ez3UpvseWTwQRBHA23gjxXR3OEAe/fO2lePggx6MGa881Z3+yFXa9hBRwDnewMVlTaqjUqhUKlv4pf3+OAbAMD55Zt3gx2uVtyo/vOmHXF0z62wfsfr9SH2I5Ftr/2pKP5vYQp91sSk4opHnzxDKi++AACx336CnLF6TiXJg60a3x74fwSyR5Wp6Br5hRLEQpBZdx6NzYRp59Ekmnx4ceqppCzeCf00C5VWS/045C88tG6dgESPKLcWLfN0a6bNmR8ME82yZTrx76uskNyaaZntGRtxkE/EPCE/DuK0E5XNDJULLppJWsV+O7GUyuls6iD4S+rnPKDf66OCtPQMQRv440T5rq/fvlHAjWK83F289Y5vBS+D5N37UrOZntMAt1a2ebjR/mawu1n+vr+0FPs7kU04gH5HWbyJXuRdtvaWBoQR+KyJSbTb91u3k1+nWbac/PoNILC4f78Sfiu3ZnJlr5w9KtfzefRrZYt7AJgxqphgEk5R8hWlMxGnnUgXpKB/++x3E72u7LCRuEd+yvvEOjmxUqNR2rLaek6dofrJSwhiVH5D/Ng9bpUYd6iE8+QhPw7iLDGhys0qraNTOSdmFv28pqem6o/pdEQcL6SlZwnawB8nah+bujf1d+8+nocwt1M0d7gfi/ePPR3PPyFV5wL+oBqWTe+K9da10GQZqlBJkTVx7JeRvibGurxrrzuk1NJxuSdQQOao2t97TrBj9bhj8R4ZOBHEguPWbB5rXCSyrx+HpucBzMV+QsXRJW7NQ35dA9zQG8MnpzPwtDxP2bqDMkAJgpgjWSoXCOjs0jq+U/0kTK2f14Y+W0ZmXv44gSCINGgDv7BEK6bURjvo+a6OPVUOusq1EkK47n5vr9fmfDJr95knQkgHutRg/dSIXhcAK1U3in43PcvmPC0oNgmnKRGI0pmIBeOzxxiqgUdmUr3spSmRUpUf8xlyZxweFIrwjHGo/NJv5ZS5pY8ZbfiPEiaeMmPVg1EtwD843Av8NADjpiGzTkXspNOtmfY/LVEA6cSJNvgkPw7idPDZYwxLUkCGyg06Avj6M+AKZpBWmYofPXP8+JGHZ0sDZC6l5qKfWi78bNEsW731Kqg0cgF5eW8j0uEFgTbwi8rQujERuZd9S9SLlCbukzHLROHWtmzugZUa8wmRR+yb5GvlnnL2k1cpnYlYSJZvtn7180kuzOkMPOi+JLNyJiDqHiw7Gfv93hPllzIOZWQbUri/efgMiDoluXdNu4NLqst9bd3iuHzHv/+KY/ODy3da/+UGgI/+wtzmWKkWNSBSba/KPp/lSvfOtgQtMtTgkzibZKhcUNI4m7TKO9tlvaHK13cHwPnsOfPST8jPrl2vwnY8rPyd3Rkn3cSJMZuQkiYvFLSBP05yOhvyc/cRzdpWT1+/VZw+90lGw8EMY02/ruu5XKH/wLT5kUxUO/6k7WiaX0g/TLbKHk0hl15ORRDEIqFygfyAPSs1qnnLHp/DqRXrDZQtVQUakxPV5DgoEM1ukBSpEw0RvRfAeTzxq0yNmwbffdQUN4qaHFta+nzbNLcBAEtLePaiJ1DQtGK9pddM1V4ZwNK7tPwkCGLujFe5WaU1Vr4Ow7gM3h999Tz1EyhUWjcfm7Yshux05t/bjiCIENrAHyfKt22n+RPgk52gDT1jxvo7cLgHA5WRkxMbf3ePK39P2dI5KpVivxudGcuPGoRd6MdOTOIGiVOeY5lto7oRJPXndAb+8YMP2qrPPGOr+W+DPILMUelV+u6dta8eOVwNr1zI2t6fJV4+nYkg5siVN4HBm1cyrshjv2w6HtTGW+XLAAA8x9opNVqV4BeX6blwiZg89puIrBR20SzbnJUaG9iynHz4zPJ0MKJsotndjWb0vP7Txv2gv0isNZRaWLfXSnnH6f5gyocNOUWtNBcTavBJnGKGZTQhm3EdDFVueUXDodKyl5dWpus/Ar5bWR4ewhHop2iWdwfBDV+mwJL0cwEhTV40fuekH+DVQiuuG4DnbNpOYCIHeB63t3nU1zM57doaA7hdc1UfE9Es2zxWIcX3gjG3JpOtuj35zuBfXgAv/nVEC5SMiQncu0HZk3xsblvlprpU0/NA33eJAzzvgPexrB4wc1S7tsbgPdx2eDjc6QAr6d3+ziTfXf1r+Z98+b3DP5P/nexTEUQK3GmvNVqtVqtVL2p+8WZL0ijBsWpucK3nWHvX5VDV8MIhueyT0xJzokNyLFCZJFqxLh8ihVEbf624bsBzHqjPcx84XrzUtLnloLRR1Kf7RyEIghhNXDYnUbnZpNWtmTY3qnLOWtvJyKicu35m3ZAgiDlDEfgFYnS7Tq24brRtzm0rosa+l4eK68fGhnjOLbNbatSLy6GN3EQTfcSvP+wMv+s51l29da8A5PRLwJPE8Hf+D5mjmn4h7RO/nEsv1VMGlRgRC0CiiV0iFTLaX7O5EytRH3JtD08lZdcO+UvtPnC8YJos21QF8aK55Xis1Cj6K8KNUtsKKkEnZKhHVL/nAVC9nVSQXWXKs9DsA/72vVHU4KbdeHKoCeXLQg0+iTNFTDbHq9xs0qpmqfuoNPzulE86u37ODfodX0RIkxcMisAfK6K5wwH27p2SwcIib8aMO+8ygO/IM9hBRwBPe4Nm2TRN06y5AihU6pvGctCM5Pzyzc3goFMrblRXYzlSuRUGeL0+RLNsbh5+I9/12p8KeXPRGYyZOPTkjz55lv6dXnwBAGK//QQ5YzX4VmzVyOFJe1+MH3X3OkDuajDKWKnaaLU+pINcgjgRVADHJ755jsZmtGJdDQspV8memKmnkrJ4J8ywKVSCuE2/Fw+IywQdvjfVflpFibZUTEt1pFO4NdO0/GBYq7HWtkxTRa/86DsJz8kz6tSSTjOJU0lUNidRuZmkVey3vXjwfJZ2QrPqJ3G2IU1eNCgCf5woO7j12zcKuFGMl7uLt97xreBlkLx7X+o103Ma4NbKNg8alOKbwe5m+ft+2ZPY3zkYRG/W7yiLN9GLvMvW3tKAMAKfNTGJdvt+63by6zTLlpNfvwEEFvfvV8Jv5dZMrkyUM0fdPQ4Yv9icV7uT01s9RSVGxKkj0gXJqLYquXhJ+bSIXld22EjcIz/lfWKdnFip0ShtWW09p85QWbhJVzGqneatwrV9P/pOEARxVEyocrNK6xyc12fRTxJOgjheaAN/nKh9bOpG1d+9+3gewtwk0dzhHmBUG7JrnOwF77uQ+DbxDb+lnGiWLUc2vSvWW9di9sbROqusiWO/jPQ1MdblXXvdoaop6RjaEyggexQA03PCbT7YkXXwjBnrYX88giAWFLdm81gHpRHV6pOi6XkAszS7G6JQabWCA0O35iG/rgHusPtFTmfgvb5A24PnxVbVjmW25/IwxAxQg0/iTJKlcoGAzi6t4zvVT8K0+hlazxNnF9LkhYI28AtLtGJKbbSDPa069lRlSspHXQjhuvu9vV6bc2/kbaPMPBFCOtClBuunRPS6ANpbVryrn9WddeF8iqtxqMSIOGk+e4yhGnhkmrnFWsDtt73x8XJNz8dsNUR4xjhUfum3csoUg5jRhv8o0vESAJix6sGoFuAfHO4FfhqAcdPwwNZyWrHeKg65KkVkmDgRgiUjNQQhTg2fPcawJAVkqNygI4CvPwOuYAZplYXy0d30x488PFsaIFPG5qGf4fdQAvp07xVsYrSozHfLTSK8IFAN/KIy1AQ0EbkvXDfCF6JZNk3LsmzbcSbehM86Ubi1siUPh+dm8ul5MKpBu9WqwRBpdPrKQCVGxGKwfLOVYMSvek5nkd9UmZUzAYVbJRb0/ID7wPH8jkyJ8ksZhxqzi3Z/8zDRoMO9a5qW80J1uTee8gNc/gP//ivw+MHlO/J73bnMdzlW1Ae4exxMzqoamKvEETOS8OMgew7i1DOJys0mrVpx3QC3y0H5+u5gzIw56mfQAL9RYsAFblFx/MlD+nmGoQj8cZLT2ZCfu49o1rZ6+vqt4vRnljIaDmYYa/p1Xc/lCv0HiTjS3CaqY9mk7aim54Fu/NJ+mGyVNarSySIp81qhslHqWs58MsEIgjgqVC6QH7BnpUY1b9njf3O1Yr2BsqXy1WNyopocB6nsGbF/xCI+IaL3AjiPJ36VqXHT4LuPmuJGUZNjS0ufb5vmNgBgaQnPXsgHFr3uXCpICYIgMhivcrNKa6x8HYZxGXy4KXHAHPUTYQN8AQA/MthgwnpMgiBmgDbwx4nybdtp/gT4ZKftW8EzZqy/A4d7MFAZOTkhhe4eBxjgt3SO6r/Y70ZnxvKjBh0BoDNAURs7MYkbJE55jmW2jWq4687pDPzjBx+0ldk7Y6v5b4M8gszRc1+dT2voApzvjEn8OoNQiRFxslx5Exi8eSXjijz2y6bjQW28C5WqwdUi0HOsnVKjVQl+a5meC5eIyWO/iRjlRwwAolm2OSs1NrBlOfnwmeXpYETZRLO7G01Zff2njftBf5GwNVS/F3T1QKHSak35qFFObyvNhYL6ehKnkmEZTchmXAdDlVte0XCotOzlpZXp+o+A71aWh4cwb/1UFZnBj6JZPsCskH7OBdLPsw2l0B8rWnHdADxn03Z4vOR7m0d9PZPTrq0xgNs1V2VciWbZ5rEKKb4XjLk1mWzV7cl3Bv/yAnjxryNaoGRMTODetWN67HHb8jO1ZBy9r/bnADzvgPexrB4wc3T591Nt4IELv5/+Z+fs893/z977hcaR5Xm+35lLG6bL9LS4U9aFyJjO4wfPUgjqYqYdSCCauC9mBBPjbdJ+SOhsMNOMY/YhO2FXD8560IUKP6gfcvJhK9XUUI1jIRekgHHnhZ7rlzoU2SsRnkFsQa7Z9UJFMqmAm6ou3DXYteB68H04J/5mZuQfpaWU/PvgBytPRChCoK/inPP7fb/Xfyn+DQ6JCigqgiLODG4HIUL1ghI0bwbtLyXYZqxu0rPN/TUxVNW9aEi89skSzeQ58SExFqpMGqVQD/Pn0oya+Mvq1aA01d21w0Qnv9cF6zaNgJHflyAIYiqSsjmJys0mrWEduwh5szMqKuesnwlk1P2IV1qCIE4O7cAvEKNrN5VCUW9bnFtmTI2DLA+5r58YG+AFN41uqVEvLEcxchOdGOB/8lFn8FPPNu+rrQcakFOvAMep4dfBf7JGlbsPG98rp0NOcVl/cPct237PJjVpJ0sn4o2RMrFL1XfG/TVF2WT4tTBRihULRa9w2poOLitA3V3bC09TCkXdDoKI5HtfEOamFDZLbdPedQvTNKMPeEQd9TwA0ttJVq8ahrzBMOzjqOfB04utemStZ/QyC/hHQ66T84F8PYkLQkI2x6vcbNLqR3XsCMvwu1Pe6Yz6GRAUBrATZHLSb/d8IP280NAO/KniO00OsFv3Sjpj4aeM6fduMYSmTv2OD3zZ6ztlwzAMo+b6gFapb+nLl4JTLi3f3gpXTpXCZvVGYrM6t8IAr3cE3ykbW4evxKde+3NfXNzv9MecOHDnjz97PvAhAODlVwDgH7SPkdNvhE/Fbug5HLcP/PGjoiO2Gv+RIFf6RYWm7wRxJsgNnOEOdvG9GaVQl8O+kKv0QtzQVUnRvBO5cmqVcCPoqJfa0FFW1xn4/lRmSCmPKLcWb/N0a4ZhBpthrcZ62zSk15JWSTyr8INq0jb8GUK+nsQFIS6bk6jcTNLqH7S95OZ5TmUDR41jRv0M0CqiLiBvm1TFdLaQfl5saAf+NJFxcMW7Gxo2Csl2d//9m0EUvNgk7z4Ues3UnAK4tbLFIz/RV/29rfIfBW1P/kHzScJr9KgjI978XuxTtv6+AkQ78FknplHuPmzdTT+OUzbtfHEDCCPuP6hET+XWDC7jQbNHxTfQKnWtIldvWak6e/P7heyeol4mYjGJuSDp1VYlF2spnwG/18UwR4z8lNdJODmxUqNR2jbbak6uoQbFSwj3qOT+/xA8SjgmCGKOTKhys0rrHGw456Gf2p0S43YiLI8giDlCE/jTRM5jh1ZkBrP3AM9DVJvkO03uAXq1IVzjhBe8ve2s1gtKGBPfCCzlfKds2sL0rlBvrUYhy5D+oJKsE8c+jMg1kZZPfq870DUlEkN7PjRkjsafW5QoUPYyQZwR0+XAuzURKBmWA51wv0VmUkxrdjcsxzinMsADAK998HneQ76oAG7PA/T/tWuED6jfKzFEa4lxW+aVlZM9DTEHyNeTOH9k5sBnqdxhkAOPmaU18Vr1+W89+LPkwM+onwFuzbD+G6MF0DOH9PMCQxP4hSXeMSUn2qHnu1z2lG1K0v3T933XPejt99qTJrrPfCJ8kUA3dLP+BAhHlBMan1zM3h7qZSJOj+XbrY9/MunB8VpQIVX5MWcoaj4Rq+FHa4wD7ZeBlVPGlF7kGCd6gWQhj1gCFXPylaqGIMpzjwcLEm7NsGyArefksuiVUqPVEqukf2vaR1gZvuBKnC4TvnfSeypxDshQucRxU0uraJSPz5m/ejn2buapn3KnSFTdL/0+O0WEODVCPSSFvEhQD/yiMqB7qZ17bU2PvvCdsmGYpmlZtj3xJHzWE323VjbF4vB8p+8iGo+234dBvUzEIpJTWcySWFTlTIB2p8Si9nJ3N/IrTrVfijfBDE1wa4ZhpdOK/FqdA+ym7DDqdQHg9z0fgLKavwxg6Y+lvOb+eAnA5fyqAuW7fwDgne8Gq6Srf74E4LuTPA9x5qSyOSiqg1hcJlG52aRVGHdY5bB9fa+fefw89TPs4w+a5p8/z0XF9sQZQwp58aAd+NNErF4Or1D3ndp2Ty3eKWhT653YDQfT9XV1TVVzOe1o17D4GzlR7FINxI4qah7oJg8VDQOqAiB7NLydfT6yw4AgiLPn2VPgZedx2X4UBBrXqz3DilXcX2E4FjWc/Y4P4KuoJv0y8K48SinUtzqFLdkFeun6lhPKiVZpbX0dDgHs3kgf+CDH+Ked8tZh7PMjvAAuezuGsQMA0O+VsBN0Y6rvAt/BI9N4JA5eWgK+rwJw/8cXYPq74VnQq60WqRFBECdjUDYLY1VOlllOLa1apXX7qRE02LPrV3E4aEosmK9+QinUW2rNkPb0AJZuncDLiCCIMdAE/jSRuW1N50fAZ812EAXPmF68CZt70FEZeXJq4u/uc4ABgaVzvEXVP+jGz0w0OAnp7/RRUMaemMb9MFis9WzTaOvVzbCoP6cy8E93P2zLsHfGbuS/DesIskcBwHed/1jnALhldPXSZoWEPw31MhGnwLX3gP571zJG+eHnP2606vIX1N2Nd2r6Ttm0ZfbR8oqCQ29nv9hqVSALMX+ohm4bW4eKOM13yuZWzQ10KD4E3ymbO2Xn/eEF9LIHCD4UHOaT98x+HFtj9J1ydE5Rt61ECShnf7mqCPHLr31QRPBWPKEbyFAupJXmwkIen8RikZTRQdkcqXLLK6GWufuzSKtbM/b6Ut5E2jzYynL6/gDMWT8F2p0S4+31Rl3dNazu92b++ZF+zhdSyAsJldCfKkqhqAOevWXZwewdgOdxa4djdPO3srrOAG7VXFlx5Ttliyc6pPh+OObWRLFVtyc+6f/LS+Dl70ZYoGScmMK9nyy18rhlhiEhipoHjuT8HIDnPeFHWJY3mD0KuDXTsjsvwgvbZiqY5PwjCpZOXrP0+vovxb+53BVBDINbRpzUL+NADnz09UAcUjKsONAWkQNfjHLgw3p6mZAcFF4qhc0SiwpJJySnMnhhSCVkvU8Qj6lVWo1SVz6h1S01WtGbKreMphokJJW61oXTIYIgzoTBHPhMlZtNWgdz4HVMzez66TvbNqhsniBOBdqBXyBGp38ohaLetji3zFh9e6D/cl8/MTbAC24a3VKjXliOYuQmOjHA/+SjzuCnnm3eV1sPNCCnXgGOU8Ovg/9kjoqOqSvLOBYLx77rbFu2VVsbWTp7rkhN2sWXNAMnFpgRnvOCdA688F2SzTUAEk5LE+fAy/8e9TywUioh2ban2wsXu0RBSsdgjrHwZ6oHgR6GrVdbFfFQiYSkom5nBMxlQtaSpwp5fBILzkAO/HCVCw+aSVpFDvx6Kge+O+WtzqSfWjB9b8xj+k6/tnOGFPIiQjvwp4pISWO37pV0xsJPGdPv3WIIN6H6HR/4std3ynL7ywe0Sn1LX74UnHJp+fZWPXrR3KzeSNRI5VZkfofvlI2tw1fiU6/9uS8u7nf6Y04cuPPHnz0f/kwvvwIA/6B9jJx+I3wqdkPP4Viu4maOigi6W2vfDf7GKVphs5RcbCYIYiFxa2IrxrQ9vdpqNUps/DmjCROSI8zJfPGSaJVWVQ+u01QbjRIT6jIsxzja/0/YPguGqSGxYJDHJ3GOmFDlZpXWOeXAT6uftPu+wJBCXkhoB/40kXFwxbsbGjYKyXZ3//2bQRS82CTvPhSSztScAri1ssUjP9FX/b2t8h+F3VEHzScJr9Gjjox483uxT9n6+woQ7cBnnZhGufuwdTf9OE7ZtPPFDSCMuP+gEj2VWzO4jAfNHgUAfO9mPf0NBlLiJ2Ohuqeo9Yi4yPyEuIAAACAASURBVCxIDvwwtIpoEAUAuLVEjnHiDTenMvDekbKWH7DaJAiCmDNZKhcK6OzSOuOLU4pp9dNH24PnhbZ8AGCbRnsuYk4QxCA0gT9N5Dx2aCloMHsP8DzIFE6IZU8P0KsN4RonaqqCEqcgJr4RWMr5Ttm0hQNTod5ajcVzJv8KZJ049mFEroleFFftdQeS73Iqk39LkDkqQqHtbUfdFBb8visiU060lUcQxAw8ewqAx7yPBRlF9WeeA5/y6QxuJSo8ZfoND3qUY9zd/zDKTtJv66Lq9DtfX4JnJ15AAeDLMQ9DnCUpU0/y+CQWgmdPMShJIRkq1+/4wDfPgGuYSw78p489PF/qI3MSPQ/9VAr1ViHyxQ9GKRV4UUgpJEgkzz80gV9Y4pYncqIder7LhBH5J0A2S/m+77oHvf1ee9JE95lPhC8S6IZu1k+NdqfEuO3ZlmmPP3g8i9XSQ61HxPlj+Xbr459McmBOZeD2rluoaJgyB57bQXu5yIHfDHPgY+2XMiE5O43I/dWj50C8F8i9b1gdXBFLoH6taHJcvRdcf8W2+JOr91p/twHgNz83djhWqgUF2KqX4mudv/m5sfPF0q1/dxGMOC4g5C1CnFcyVC6siJxNWsWVrbLakO3re33gUvY589LPhG++XJG4EDZGFwbSzAsGTeAXldSG9cDOvbamg3flF3GXk6mY5UTfrW1b3AObmzorhXpDFRcFwPTq5p3e9nxm82fN6+u/HOo8T6JJXARSYcWs1KjmTWt8DadSqDdQNoOw4vgGu1ZpNdRwaIyhHga2fADA770ELuE42E7Xb+t877HjbxQUMba09EUYcry0hOcvxQ0PBBnnSg/u0gYSQRDzZbzKzSqtWqVVhWEF0qdfBc9w8Zirfvq97lw68AmCmASawJ8mOZWJytEcnN1kDvzmnd7udk8t3ilM37wkdsPBdH1dXVPVXE472o2r8qgc+LEnDuC7n9z/xaPn0hSvd+RDFv4rah7o/uvntbIpZ+FM//N3w24pNR/rLnVrhoVqFYleKiW3lmfiB+J1m7ufvuulO60IgnjzZOfAA0AeB2XD9iAn3lqlqnOpGp5tNkuNViX8xWVqLnpFZLO0Qw6sZMbwnbLFWamxiW3TjuUYi16l2Cux73T34iWr3//LxsOwPamcWCsUrZ/i01L2zn8WC+XEcfEgbxFioRmU0ZRsJoUlUrlYDjxOLq1MVX8AvB6RAz9v/TzqhU2V8WSRWSD9nDukmRcPmsCfJjK37RcGfxX/2PO4Zf4T8AI6KqPORao13d3nsktcZDLFF3D9g27sxP6/vBxeRDXuxDTufdOKRcl53DLD3tScysAf7cSz7T0v/LsUtXxBJNjrXyXb4t2aGS0cJM8990zSnEmtm8S5gdvtIEQIYfOmtOvwnbJp1tRQUzzb3K8KMyS3ZljhkJwgixLN5DnxITFWxoiJf5C05DuDY6PkQ1avBqWp7q6dSnRC8OmYwn2CIIhJScrmJCo3m7S6NcPiYa5b2bRHuwnNWT/9XhcMTcOQs8GZFmwJgpgUmsCfKkrhz5dtrw9cWr593/zJ9esAcHi41Xhw2H+Fy/qd4aWiQWB7bS0ysbN4YHHiAwDfd+9o0gKuJrqlEsVWL3/nC1+T5bgL/QQnhvi1ege4dP1efWtDEU5zlu0FQq58FwBw6frt+1s/uY7D/1R+sOe9urR0VQEC0yp76+f/b/+LYwB4+g92P3xt9j/5qcWBK7eq//ddTfF/U7u/w58jOPeCMGpmTl1JxOKRMrFL1XfG7TmcZqJFfSC1nZUCURNNP0JbEhPkRN6672zbHguzhJXCZqlthq+LEzLgEXXU8wBIbydZvSoL5VnkFRri7vORbqMTQg4XbxbyFiHOGQnZHK9ys0mrPEteR5bhd6e80xn186jnwdOLrXq0Emv0MhugRkO/xfOHNPPCQRP4U8V3/llYirzq721t7aVGX3QPfBQUUeV+qdeX/el6tVEpFPW2xbllxl6rgzDOYHpvDql9D3vcX3HT+C/Xt5wtRCX0WScOcPjxf3kB4NXhjil7nwS8+ZuKtgH/GwDAq8O9LSN6rlfPv/BxXQG0yu1lvidn7wD6/UF/0uNHlvko+OJSdC5BEKdLZt95fG8m2MNJ2mnko4OHdkSK2p9oghwrtzzqpTbEB95bJyDlEeXW4n1Bbs2wOAs2w3ynbBp2sg5JRB2PWE0lFgHyFiHOGXHZzFC58KCZpNU/aItIjZCcyqYOx5xRPxPBc6mFWeLMIc28eNAE/jSRbvL/obHWi7XAyx74XDJJ7tV//Xv7ORDkwGfYkEBZff+K7R2nP/Z6R1iNf/Dq+f/n4/+Y7ESkJNf/4vmr9HGCd/53QC7R3ritf7nHg+SR2+/yvSfhpa7dvHH7v3+590SMXl6p/iJ8Yz76+jmwssI6naB9vngT1g4fchuTcI66p6griTjvxFyQ9Gqrkku1lE+J3+tiWJZbfsrrJJycWKnRKG2bbTUXzs43g3dKuUcVf80ceLsmCIKYGxOq3KzSOgcbuZPoZ5Jhb5MEQcwDmsCfJkEOvKJpFa0wst19eUXBoff8O5PnwH9+PBjn7ulrGhRtIAc+VkKfcWKaaDk4QB5bfaAhyIH/N/9X5YOfVKIDuntPwnJ8rfCBBvxE/lX64V9FNfri3Ov/rv5AiV2cjSrlJwhiUXBrFk/0Ovonu56i5gHMpXcysR/k1jzkiwrg9gbsMXMqQ2y1UNTPk4HmokPB78Q5JUvlQgGdXVrn8+I0q34Siwtp5gWDJvALy9uTAz9nzlM/D3UlEQvGs6cY6IFHZlG9sOIQCKnKj/kewhMjKov3ozXGgfbLwMopc0qfCNoIbiUqPGX6DQ96VYNMAunuf2hYT4Inu63Hq05/8595+APITrAjFgDyBCUWkWdPMShJIRkq1+/4wDfPgGuYQVpFKX58Nv3pYw/Pl/rIXBGdi34KFb+t873gb8fV5ZNaiRBvgHAaT45L5x2awC8qb1MO/FsLdSURC8ny7dbHP5nkwJzKwEP3Jd/ZnkxNtDslxu2g7tLdtT1W2tSAgfZLsQ81xg/e/dWj50A8Kcm9b1gdXJEu97WiyXH1XnD9FdviT67ea/3dBoDf/NzY4ViR38CtGTtfBPbJbs2wDNAc/jxBnqDEOSBD5frBMbNJq7iyVVYbsn19rz88gyjGvPRzdX3Ztv8RjVZLAeB/8lPz0fOr/5bUc9Egkbww0AT+NAlz4If4MflO7U3lwM/zRDnjTweEKImkd8HRYLHVUE5yLkEQZ4isBQo27FmpUc2b1vgaTqVQb6Bsyi7QhJxIk+OwQTR7GzzWJxrh914Cl3AcdJnqt3W+99jxNwqKGFta+mLHkGacS0t4/jJycF5awnN5I4lVBoIgiDkxXuVmldZE+zp0/Sp4hoPSPPXzqNfHFcbNoHrrhs6e8P/hYoPm8ATxJqAJ/Gkibd+bzo+AzyIXO+HaZnPvzeTAJ+uj+pEL/bQ58HDDwinPNo22Xt0Mi/pzKgP/dPfDNn8inehu5L8dHib6JVLlWql6MrdmWPzyxcmBz4a6koiF4tp7QP+9axlH5HFQNmwPcuKtVao6ly+Bnm02S41WJXzDZGouekWcKRc4SwZ8p2xxVmpsYtu089E9i/W/mLL5TncvXrL6/b9sPAz9RZLWULGhk3COrDQvDOQJSiwKgzKaks2kxEQqt7yi4FBq2cmllanqD4DXK8uDQ5i3frr7HKy0mbyP0S+02ZB+viFIJC8Sf3jWN/B2oRSKOuDZW5YdetADnsetHY7RyUXK6joDuFVzpY9JIgceAMD3w7F4nDsA9P/lpcyBH0bGiSnc+2Hbk7htbpllRx6qqHngSM7eAXjeE36E5fW0l7PvfNwZeDw1D3j2tuP6YWTJi0T714UnnLcPragniAWC2+31RqvVarXqBSVo3mwJGiXYZs0Nj/Vsc39NDFV1LxoSr33itNQ58SExFqpMGqVQFzcxhFETf6VQ1OHZu/L7ubu2F2hNxhBBEMQJSMrmJCo3m7S6NcPielWcs962Myoq56qffq8L5FXFrRkBsbslCGLu0A78AjE6/UOZNQc+5AU3jW6pUS8sRy70U+XA+598NDDzBjzbvK+2HmhATr0CpBPpXicv4da2LT6QWhcuNNtWbCtspfjW1K1SSxKxYKRM7FL1nXF/TaeZaFEfSG2PViWFa4eoAHV3bS88LZEX7DvbtsdKDXlFpbBZapthJ+iEDHhEHfU8ANLbSVavGoa8wTDsI3NoSsiE8gwgT1BicUnI5niVm01a5VnyOrIMvzvlnc6knz4AcMvoloImeKdsGrUZPUToF/ZNQSJ5gaAd+FPFd5ocYLfulXTGwk8Z0+/dYgBvijXYfscHvuz1nbJcxvQBrVLf0pdDM5JLy7e3wpVTpbBZvZGokcqtMBHA6TtlY+tQJrh77c99cXG/0x9z4sCdP/7s+fBnevkVAPgH7WPk9BvhU7Ebeg7H7YNgVdmtGabFoVf1YdVcWqVR1eMrvjf+ivqmCOKMkBs4AclXsPjejFKoy2FfyFXaE3PoqqRo3olcObVKuBF01Evteiur6wx8f6q9HLlLtC33tGRVj8StGYYZbIa1GuttM9wqyhgiFp9Ri560GEqcPXHZnETlZpJW/6DtJTfPcyobOGocs+onkIyIL+rhSy2xIJBIXiRoB/40kXFwxbsbGjZSOfD++zcVRSif2CTvPhR6zdScAri1ssVDg1K86u9tlf8oaHvyD5pP+vGLHXVkxJvfi33K1t9XgFgOfMaJaZS7D1t304/jlE07X9wAwoj7DyrRU7k1g8fiQVWZN+/WwIf02CrAkdjqQ82w+DtDfnwTcr66p6gliTjXxFyQ9Gqrkku1lE+J3+sKh43UNfJTXifh5MRKjUZp22yrObmGmnjLrFd7htV07mirByOH3ppqIIIg3jQTqtys0jq6lHNiZtDPTQBDOo48SogniDcETeBPEznLHbq3HMzeAzwPUQGn7zS5B8gpcOAFH6SQBDHxjcBSznfKpi1M7wr11moUsgxZ6CTJOnHsw4hcE70ortrrDnRNicTQwDRVK2Tambg1i8tcOtrxIogzY7oceLdm8YSD0gm3WxQ1D2Bqs7thOcY5lQEeAHjtg8/zHvJFBXB7HqD/r10jfED9XomB946g9jzgRm/bMKS5aKmxqYohegM9J5AnKLEQZObAZ6ncYZADj5mlNeFU//lvPfiz5MBPq5/Kn2HoqsSoHwJxVpBIXhhoAr+wxDum5EQ79HyXy56yTUkp1FsFwPd91z3o7ffanE8W7T7zifBFAt3QzfoZkNP3+exznbNOHmpJIhaOiXPggeSui5Cq/JgzFDWfiNXwozXGgfbLwMopY0o/mGMsNq+C9kyxk7VS1RBEee7xYEHCrRmWDbD1HHLqMvCEfxudZZrLYoggCGJeZKhc4rippVU0yseXHL96OfZu5qSf768zeLFlCXEa+7cT/yEhTpPX138p5vB/cPg3NIc/p1AP/KIyYAKa2rnX1vToC98pG4ZpmpZl2xNPwmc90XdrZVMsDs9l+i6zTDbfzjJVakkizjE5lcUsiUVVzgRod0osao90d20vcGRKtV+KLX59tKOlWzOMZDoGAL9W5wC7KTuMel0A+H3PB6Cs5i8DWPpjKa+5P14CcDm/qiBluqnd0S+jH16FOA/8weHfxD1BU18SxEIwicrNJq2i8dwqh+3re/3M4+eon0phs8TCOxaN80u3NjcmuGvilCGdvBjQDvxpIlYvh1eo+05tu6cW7xS0qV8XxW44mK6vq2uqmstpR7uGxd/IiaJ4fyB2VFHzQDd5qGgYGNuOJRaWkSq94pbBZ4qNJgjijfHsKfCy87hsPwoCjevVnmHFKu6vMByLGs5+xwfwVdTJeRl4Vx6lFOpbncKW/KW/dH3LiTvBb30dDgHs3kgf4yDH+Ked8tZh7PMjvAAuezuGsQMA0O+VsGPLHS/1XeA7eGQaj8TBS0vA91UAR71j4Ib+nUiKlpYAfONTBT1BELMzKJuFsSonyyynllat0rr91AhEjF2/isNBU2LBfPVTlHSqNUP60+PS9a2Hd0k6CeJNQRP400TmtjWdHwGfNdtBFDxjevEmbO5Bx+hG8dTE393nAAMCS+d4i6p/0I2fmWhwEtLf6aOgjD0xjfthsFjr2abR1qubYVF/TmXgn+5+2JZR8IzdyH87PEz0S0zYF/XtuAMuCNSSRCwO194DhphMxkb54ec/brTq8jff3Y13avpO2bRl9tHyioJDb2e/2GpVICsqf6iGbhtbh4o4zXfK5lbNDXQoPgTfKZs7Zef94St5sgcIPhQc5pP3zH4cW/7znXJ0TlG3rUQJKGd/uSpMNMH+zZ36B5XorJkN+c6XlebFgAxBiUUhKaODsjlS5ZZXQi1z92eRVrdm7PWlvEkFYyvDon/mrZ8CrdJqVUQW/X/Yuj7zz4/0881BOnlhoBL6U0UpFHXAs7csO5i9A/A8bu3weK5n+rTVdQZwq+bKiivfKVs80SHF98MxtyaKrbo98Un/X14CL383wgIl48QU7v1kqZXHLTOo1BJb8Edy9g7A857wIywPeJL6zsepOHmlUG+1GqWBrBNWqr5V2++vr/9S/APw/K//etS/s75N4m2AW0acVJTaQA589PVAHFIyrDjQFpEDX4xy4MN6epmQHDTUJMsyJyWnMnhRhKWsBgriMbVKq1Hqyie0uqWGjLDLPosgCGJ2BnPgM1VuNmkdzIHXMTWz6WeEu89HujUTBDEvaAd+gRid/qEUinrb4twyY/Xtgf7Lff3E2AAvuGl0S416YTmKkZvoxAD/k486g596tnlfbT3QgJx6BThODSd6SuG7tW2Lp48BZBl9Yk1Xr1L1PEGcESM85wXpHHjhuySbawAknJYmzoGX/z3qeWClVEKybU+WjBHdVVG3rSClYzDHWPgz1YNAD8PWq62KlnnWtJAD5RlAhqDEwjKQAz9c5cKDZpJWkQO/nsqB7055q7PpZ3gPImluxG7UhNAv7BuEdPKiQDvwp4rvNDnAbt0r6SzacmZMv3eLIdyE6nd84Mte3ynL7S8f0Cr1LX35UnDKpeXbW+EMVylsVm8kaqRyK0wsmPpO2dg6fCU+9dqf++Lifqc/5sSBO3/82fPhz/TyKwDwD9rHyOk3wqdiN/QcjqNVXLdmmBaHXtWHVHMd9byh1fYEQSw2bk1sxZi2p1eH1tJMQ5iQHGFO5ouXRKu0qnpwnabaaJSYUJhhOcbh/v/Is4jzABmCEueCCVVuVmmdUw789PopOOp5Q/LgiYWBdPLCQDvwp4mMgyve3dCwkcpF99+/GUTBi03y7kMh6UzNKYBbK1s88hN91d/bKv9R2B110HyS8Bo96siIN78X+5Stv68A0Q581olplLsPW3fTj+OUTTtf3ADCiPsPKtFTuTWDxzKUVZk379bAUz22fq8L5HFUK5vcA8D0kyzfXoDuqf9t9NAFeDri4rAgOfDD0CqiQRQA4NYSOcaJN9xcPOx9+FkEQRBzIkvlQgGdXVoTOfAzM5t+BvXzJ19DIAhiDDSBP03kLHfo3DSYvQd4HmQKJ8SypwfIKXBQUxWUOAUx8Y3AUs53yqYtTO8K9dZqFLKM5F+BrBPHPozINdGL4qq97oBjXU5lsb8lWmG0P5/osLJD+wyP2ybvZVXxEgTxRnj2FCIEIvlxRlH9mefAp3w6g1uJCk+ZfsODHuUY/89//FvDEmVGrNQo9uJVp5GzM7AMeRZxTiBDUGIhePYUGV69GSrX7/jAN8+Aa5hLDvynjz08X+ojc0V0Sv3s7n8YZc/pt/W4fv7mP3MEf0AyO7GIM4R08mJAE/iFJW55Iifaoee7TBiRfwJks5Tv+6570NvvtSdNdJ/5RPgigW7oZv3UiJxRVqpuigw9362ZFudN5442yz7cBWjjGdGtAFyIpyMWnuXbrY9/MsmBOZWB27tuoaJhyhx4bge/4SIHfjPMgY+1X8qE5Gw/S/dXj54D8dYc975hdXBFLIH6taLJcfWevP6fL9t7h8fXt1pb1wH3Q8O0IL+BnLuL107f+Zlp9/GDSR6HOBtGvYDS+yix0GSoXFgROZu0iitbZbUh29f3+sCl7HOm08/iim3xJ1fvtf5uA8Bvfm7scKxIgXZrxs4XMidPcWuGZYDm8IvLKJ2kif15gSbwi8pA62Vq515b08G78ou4y8lUzHKi8KLzwEqN+Uhz5NYiv9YqVZ1byRVqgiAWjlRYMSs1qnnTGl/DqRTqDZTNIKw4vsGuVVoNNRwat40T3zAP8XsvgUs4DsKQ9ds633vs+BsFxXf+uY/c9SuHW0Fa8WXgXSG2OTUHHIXlB3q1+p5lNR1/pnVE4k0SvmLGv6TXTeK8MF7lZpVWrdKqwrAC6dOvgmfkaEyrn2JsaemLMCR+aQnPX/Z8aIrvNDmWloL9h+QqLXEeIF09X9AE/jTJqWwgzz3Ad2rbPbV4pzB985LYDQfT9XV1TVVzOe1od4gqz+VEOeNnqZJWRc0j7XV6NNgtNTE59URWWARBzEZ2DjwA5HFQNmwPcuItl9sAAJ5tNkuNViX8pWdqLnpFTKvGRGSZyPlO2eKs1NjEtmnHcoyF9MReiX2nuycWBMXQX3z00VZ0DdNuH/iFAg7aR8l79J10pStBEMRYBmU0JZtJRYlULpYDj5NLK1PVHwCvR+TAz6Cf4nt8/y8bD8P2zrJpx64ZGyII4o1CE/jTROa2NZ07WgGus9tsB2nw7Mqyd9yHjtGN4qmJv7vPAQYEmUyJHI+D7iS3M/WJbtj55Nmm0darm2FRf05l4J/uftiWUfCM3ch/m3z7DnbuAeCffu36WrzxPubo4tYMiy+DjFAIYhHhdjsIEULYvCntOnynbJo1NdQUzzb3q8IMya0ZVjgU/NK3Ckr6nPiQGCtjxMQ/qN3xncGxjIl/1ppAwsF52LrkhJDZ5Jvj3X+pDv38Dw7/5ss/tYYOEcQZk5TNSVRuNmmVMbytihbOrkfthsyin7L6Pyjtd3ftMBEvY2hqSD9PH9LVcwfFyJ0qSqGoA55tFgzTsoPZOwDvuA9cuvpnw0tFldV1BnCr5koLOt8pWzxhccL3wzG3Jrqlur2YYV3iizjjToxw74e+JeKeuWWWg/QQRc0DR3L2DsDznvAjLEc36NZMK+qwf9GxzJobezrP3hZPJzNH+yPd/t4Glv7+70f9O+U7+YPDvxH/Tvn7EmcKt4w4wa+qJG7P4TR5/GtldZ2B70fHR3nA2poeaou7a3vhaUqhGAUR+c627UUxRUphs8Q8ezd5A+PIqQxeFGEZ2GT2jjKHjnpDm4mGpWoSBEFMRUI2x6vcbNIqzgq8iUTI29RkiCRE9X+pK/9CWN1SoxUuPGQMEQQxb2gH/pTRKg39n0z+YnDkMl59sVNzN4b1eyqFot62OLfMWH17oP9yXz8xNoBnm0a71Kgnes0nOTHA/+SjztDL3ldbDzQgp14BjlPDr8OznSaXPnXYNSzOrsALXerEwm3q6ZZPkiRHnBzqhnqLyew7j+/NRP4VcTuNfHTw0ExiUfsTLdBplVZL/veol9q1Eb7KkyVjRHeV8IiSi4LjhuYKmU2+Qf5l5Aj92IkFJS6bGSoXHjSTtPoH7bglPMRkvDvlrWaKpFszLM6CYgLfKZuGLf9eZAxNC/0inwGkq+cN2oE/bfyD7gtgieWisibG9Gqj9YsSQ7AP9Q3g2b+Kr8dqa3ng8lLw5dKN6r182zQMwzDKu/hpVQ8vdwlYuXePiTl7zfX9g7YnbEi93lGw2W6bNRdQCpuxEwEgt7IyfMvJf/zZCGv0l1+Jx2ofI6ffCK/Gbug5HMtVXPFnpbQZNvjnf1yKr/FqlVajpAd/dS4DuPHXtHJLEIuPWxP7Labt6dVWq1E6kXuF3+sK5YqYyZ5Tq7SqenCdptpolFjwBj1yaITxRlbBPXEWjFpGpOVF4lwwocrNKq1DF02nY6RI+k6TI6odkHv8vOn4WUPEeYB09dxBO/CnjEyE+9v64KpkYbOxKtLgCx9Xe4bF912Eu07uPgcrPZDhHN3/853mTlCQ7nF7q1tqtFphQvzLx49la72aU9ADoEQt5sL+WdqOKDk1cQ9Hnc7wbDjl7sPW3fSzOGXTzhc3gDDi/oNK1MTv1gzORSJp3M9Oq7RaFcCt7YDH8koVFUdi5w81w+LvTPoDHYS6p04OdUMRE+HWLJ5wUDrhy5qi5gHMYnY3gBQagVvzkC8qmUM+IApRQx9Rv9edy+swQRBEQJbKhQI6u7SOd6qfhOEi6Q5aE+dUBt47gjp6CCShBDF/aAJ/ysiJ7tCaIjF7ByBD4mK2dWL+HpZceZxDrzYqmgLfdbYt2wudQwDA8xBan2TLvtgaD93ofKds2iNs8gdOdbZtD3pRA+SbbmqrKqcy+bcEmaMyQNTiMpduunZXgiDmyLOnQJikFpFRCRmz4pALlPkx30NR8wlXTrEUWG1VNPHKlzB+l1ZOmVP6f/098PQZEL/DWOEp02940Kta9pAoYxWvm75TNnvFKlL1qMSCIDaF4m0+tE1ELBDPnmJQkkIyVK7f8YFvngHXMIO0xjVM8OljD8+X+shcEZ1cP8V7235oZwzot3WhkjmVAf/zH//WsEQFJys1ij0S0HNFSlcHRZUi4hcKmsAvKMkZfGr+DpHBLhygNBEXmph2x6xPJsDrNned3trqqpZOZB+JLxLohm7Wz4Ccvs+nap7adeYAdUO91Szfbn38k0kOzKkMPFw9FIt6E5CMCHZ3bY+VNjVgoP1S7EPp1UxpcH/16DkQT0py7xtWB1eky32taHJcvaeNGxLf26qtVWHZHlZ+bXXGfWvizCCTDuK8kqFy/eCY2aRValhZbcj29b0+RAflaKbRT6VQXLEt/uTqvdbfbQD4zc+NHY6VakEBCn++bO8dHl/fam1dnZbl5QAAIABJREFUB9wPDdMCCeg5ZKiKkt4uIDSBX1TiM/iB+XuqrDO1mz1F16b0j/O453HbBsD00rgs+iAOTm6Yn5ggjJR0fnF4ff2XQ53nSa+JBIpYPgw27FmpUc2b1vgaTkV08piGLU+LNti1SquhhkNjDPUQRSHH8HsvgUs4tuVF9Ns633vs+BsFJWNIfO/bTw1LNIl0OjMaMBEEQWQxXuVmlVat0qrCsAJ906+CZ8RoTKufYmxp6Ysdw9gBACwt4fnLng8Nzj/3kbt+5XDLMMR1LgPvkoMIQbwpaAJ/yuRUNhDpHuA7te2eGsyfoxk8Bubvc0SrtBprzm7TFi31Hrct3hv93irrqliqpHVYYHKs8T1r1HfaHgA7/FMGQNTwpr8JQRBvmmvvAf33rmUckcdB2bA9yIm3VqnqXL4EerbZLDValfC3lqm56BVxpl/orNXIYO1vE9umnY/uWYhLTMV8p7snSlYzhkQg814/vOBJ6ovIieONQiYdxEIzKKMp2UzqYKRyyysKDqWWnVxamar+AHi9sjw4hJn0U3yP7/9l42HQoukEjkrirL/46KOt6PKmnWgUmBzSz4WC9HYxoQn8KSOj25rOj4DPmu0gCp4xvXgTNvegIzAOCWbwaxicvyetloQx3givJdFvGu+KOuole6kUrVDRChXA992DXcvmo7rg3bDzybNNox22zkPWe326+2FbRsEzdiP/bfh3KXt0FN9mDRJvmLHdUMRbCrfbQVIQwuZNabnhO2XTrKnhq59nm/tVYYbk1gwrHBKvfbJEM3lOfEiMlTFi4h+0/PjO4FiGuIweyrogQRDErCRlcxKVm01a3ZphcTn7lrPrUe71s+inrP4PSvvdXTtIxDvKOIsgiPlDE/jTRhat21uJZSvP49YOABZPP9fulBhvN5ve4P67Z2876mZBmtjZo43xAvi+7Df1nXKsZEoGd1ZFwJui5FSVARiqwu790LdE3AO3zNBcSiwT8PAAz3viAcvyvjNHC/VWIZ50KmElap46e8J5O83k3yZSJnap+s6YxYbvNBMt6gOp7ZGiaWs6uFx5dHdtLzxNSKJsiPedbduLGmqUwmapbSYsOidgwCPqqOcB6B1lDs1Vbcgq4s1CJh3EOSMhm+NVbjZplWdVAlePerVnWN0p7zRbJGX1v6yTZ+EKw1yllX6LFwvS24WEJvCLRbK3XUj2sN11xmBbZlRznuUmJ3byuW1ycTzTdcbl67kwk0pcCsPL9f1PPuoMXtuzzftq64EG5NQrwHFq+HXwn+xRUUMQzBLkCjJVzy8IZF7y9pHZ/B3fZYlsL+NrcPno4KEhbH6vm1hy1CqtlvzvUS+1Xjnw3joBKY8otxatWWYMEecHMukgzhlx2cxQufCgmaRVxArFfd9zKku3L44lUyTlpo8sJvCdsmnYerVV0UhaLy6kt4vJH571Dbx1+E6TA+zWvZLOosomxvR7txjAm44PwK0ZhlFzoayuM4hXXbdmGGXHl/kkuFm/vSy9RS9f1bfCt+1+xwe+7PWdsmEYhlFzfd9pciwxIelMLzXqP/raF6EhAJRCfev6cuhSeunyyr3h6aSPP3s+/IFefgUA/kH7GDn9RvhI7Iaew3H7wB8/Kv+eUfEVQZw73JphGIZhmLanV1utRmlUveZE+L2uaNCJSJfmTIRWaVX14DpNtdEosUBhMoYIgiDeNBOq3KzSOnTRdDpGiqTvNDlYaTN4R1QK9aoevLeStBLEaUI78KeM7Fcv3t3QsFGoJMfevymj4EMDu7UgNt6tJRrhv/ltea//Svz/xRd8K0gNERYo6D4Ufw6YmlPQA/D99erDeli/FbNJ8Z3y1mGYXIJXLzo7u+7G4O6bcvdh6276WZyyaeeLG0CYb/9BJXokt2Zw2XufPSr+nuVxVCub3APA9HgnwbSQ/ckcIfMSIgu3ZvGEg5J/suspah7AXNwrtYpoEAUAuDUP+aIyfog4L5BJB3FOyVK5UEBnl9bxTvWTMFwk3d5ARWhOZZHJEknrRYX0dgGhCfwpc9Qb3a8uZ+9AMIP/9f1uR3Q6pebv6Hc8vdqoyB54y/YS/VOeh7AxKVv2RcVV6EbnO2XTHmVhlz7V2bY96EUNkFPw1FprFG6HzFE5v/fscD7ocdvMssInCOIN8ewpBnrgkVlUz9YjYRILlPkx30N4YkQ6I5YCq62KNthIGVg5ZU7p//X3oqYofoexwlOm3/CgV7XphjyrtkYadFZM+KZI75HEIvLsKQYlKSRD5fodH/jmGXANM0irKMWPt51/+tjD86U+MldEJ9fPnMqA//mPf2tYIpuOlRrFXrxqP55Lt4y4tBIXgLje0mT+zKEJ/GLi7nMAnY7slHJ3U0b0l/WGNIBXNOFTkph2x+xSJsDrNned3trqqha1XY3Bd2um7WW23k+O3+sCCK304Ls10+JculpNDZlqzBMyL3kbWb7d+vgnkxyYUxl4uHooFvUmQHhvBL/h7q7tsdKmBgy0X4p9KD3bz9L91aPnQDwpyb1vWB1ckS73taLJcfWeNm5IvrMGuUrfgFsGaB3xtCHTDeKCk6FyYTnkbNIqrmwFJZluba8PXMo+Zxr9VAp/vmzvHR5f32ptXQfcDw3Tgrx1OXcXa72+8zPT7uMH0/9wiIWHJHpBoAn8YpJTGeABy7frhSHzd7zzJ+kapnjd1ORtR9IT3+Oex20bANNLQRD9KHy3tm1xD6zUmM+rbXrZQJHxpzMmiBJzhMxLiCykzXGwYc9KjWretMbXcCqFegNl0zRseVq0wS5NjuXQGEO9xIZPiN97CVzCsS0vot/W+d5jx98oKBlDgTd+vaD4DoD37pW+uzOlfR5BEMRYxqvcrNKqVVpVGFagb/pV8KPRR0+tn84/95G7fuVwS7rQ4zLwbpAWnAOOwvItvVp9z7Kajj/TPgxBEOOgCfwpk1NZong0ju/Utntq8U5BU5RCvaXWDIv/zgWwH5+/X3sP6L93bV43pFVajTVnt2lzDwA8bltZ1euyroqlSloVNY+01+lR1C2VPTqEnHoiKyyCIGZjvL7kcVA2bA9y4i2X2wAAnm02S41WJfy1ZmouekVMq8ZEZK1G+k7Z4sGGeT66ZyEuMRXzne6eWBDMGELkjS/WFH2nPHNDKTlxzAaZbhAXgUEZTclmUlIilVuOORSdXFqZqv4AeL2yPDiEE+jnX3z00VZ0DdMW+nnQPkreiO+kOwUmhvRzYSGJXhxoAn/KKKvrzPZ40/kR8FmzzT3ZZMT04k3Y3IOOwAIkcLJbQ3r/PfVWKY3xRkyGRb9pvCvqqJfspVK0QkUrVADfdw92LZuP6oJ3PzRkFLxnm0Y7bJ2HrPf6dPfDNn8i7PPYjfy34d+lzFHfKZs2bt1b//qxXEVgbOWdrOk9cZqQeQmRgNvtIEQIYfOmtNzwnbJp1tTw1c+zzf2qsDVya4YVDolfelmimTwnPiTGyhgx8Q9qd3xncCxj4j9iaKCBKHuRkSAIYlKSsjmJys0mrTKGt1XRgu+CUbsh89VPACkH/GH7OgRBzAuawJ82smjd3kosVXket3YA4VgXoN0pMd5uNr3BZHbP3nbUzYI0sbNHG+MF8H3Zb+o75VjJlMz0lN3nipJTVQZgqEC79+XsPbgHbpmhuZRYJuDhAZ73xAOW5X1njgrflUc7UYeX53UArGQ/EfGmGDpXp3n720TKxC5V3xmz2PCdZqJFfSC1PVI0bU0HlyuPslhdnCYkUTbE+8627bFSQ15RKWyW2mbConMCBjyijnoegN5R5lBSYoVQslnzMMghYkbIdIO4mCRkc7zKzSat8ix5HVmG353yTmfTT6Q2hiTeoLZOAP2yLy4k0QsDTeAXi2SCp5DsYbtAjMG2TDv8OstNTuzkc9vk4nim64zL13NhJpW4FAaXCwDA/+SjzuC1Pdu8r7YeaEBOvQIcp4ZfB//JHFXUd4bd9+/nEoZCTAF5kxAAxvSdxzdgol3rmGlx/C1uaCaxHxWrAxDZQ/K/R73UeuXAe+sEpDyi3Fq0ZpkxFCcw6ZxDnB0xDWS6QVxM4rKZoXLhQTNJq4gVWo9twORUNvUm+Mn1k7i4kEQvDn941jfw1uE7TQ6wW/dKOosqmxjT791iAG86PgC3ZhhGzYWyus4QBMEbRtnxZT4JbtZvL0tv0ctX9a3wbbvf8YEve32nbBiGYdRc33eaHEtMSDrTS436j772RWgIAKVQ37q+HLqUXrq8cm94Ounjz54Pf6CXXwGAf9A+Rk6/ET4Su6HncNw+8MePuvsdIHc9HGWsVG20Wh/RuzNBLDpuzTAMwzBM29OrrVajdCL3Cr/XFQ06EeZkxvZJtEqrqgfXaaqNRokFb9AZQ/IenLJhcVZqkP88QRBzZ0KVm1Vahy6aTscs+jnCuGhyS2WCIKaCduBPGdmvXry7oWGjUEmOvX9TRsEH7e+VtSA2PhUE/81vy3v9V+L/L77gW0FqiLBAQfeh+HPA1JyCHoDvr1cf1sP6rZhNiu+Utw7D5BK8etHZ2XU3Bl9clbsPW3fTz+KUTTtf3ADCfPsPKtEjuTWDy977zFF3nwP6T7fm9bZM9iezQd4kBKbNgXdrFk8YF/kn++6KmgcwtdndsBzjIMkD8NoHn+c95IvK+KH/9DNjrw+ILqVV2oA/A8h0gzj3ZObAZ6ncYZADj5mlNeGQ9PlvPfiz5MBPrZ9+7Hv7TtnsFRtqdy7LCcSCQRK9INAE/pQ5CmbkQ5CzdyCYwf/6frcjOp1S83f0O55ebVRkD7xle4n+Kc9DaH2SLfui4ip0o/OdsmmPsrBLn+ps2x70ogbIJeXUUmsUbofsUQBMzflu6IXPmF6M/PEIgjhdJs6BBwC2HgmTWKDMjzlDeGJEOiOWAqutijbYYxlYOWXMpQdzjANzD6GCwqx5paqNG/rNz429PlaqrQca4NYM08gI5CDeKPGXQnpTJC4UGSqXOG5qaRWl+PGu869ejr2bOeln9L2PRAOS6BRYpw34iwWp8eJAE/jFxN3nADod2Sk1EAR/WW/ICa6iCZ+SxLQ7ZpcyAV63uev01lZXtQEf5lH4QY/oyd9u/V4XQHvb9MIaMi/hjzctZKQxI+RNQkxLTmXg4eqhWNSbAOG9IX3r4O7aHittasBAj6XY4o9ZOaUZnmNcq3OA3ZT2zb0uEHhqZAwdbn3yBS7rjQda7B4pBv5MIVcO4gKSoXJhOeRs0iqubAUlmW5trw9cGn38HPUz+N5B8daXv7Y6WdJNnDdIjRcNmsAvJkGN0vLtemHI/B3v/ImSPjpWNzV515H0xPe453HbBsD0UvFOIXPz23dr2xb3wEqNuW1OeR5kRUH0Daa1niZOBnmTEGN59hR42Xlcth8Fgcb1as+wYhX3VxiOhRb1Oz6Ar6JXxMvAu/IopVDf6hS2TMMGgEvXt5zwPU+rtLa+DocAdm/kJniQY/zTTnnrMPb5EV4Al70dw9gBAOj3StgRYcWjh1a/eP4KeMXNRPeA9/dylYEgCGImBmWzMFblpIP81NKqVVq3nxq2vDK7fhWHRyNua676WVDitfUAOp1ML1SCIE4ITeBPmZzKEsWjcXyntt1Ti3cKmqIU6i21Zlj8dy6A/fj8/dp7QP+9a/O6Ia3SaqyFxevwuG3xjLpRaYjKUtvjwwI/YynKWaOyHyxWMq9olc1S17S7ZENPEKdMtr5cew/gh5//OJYDvxvv1PSdsmnLXZflFQWH3s5+MQor5j+UDZG+U946VMRpvlM2t2puLAc+HILvlM2dsvP+mBx4RK4eAezHMYnynfLYoVT5URDUPGMbPDlxnBxy5SDOK0kZHZTNkSq3HDkUufuzSKtbM/b6qRz4leX0/QGYs34mLih6ok4weyf9XDRIjRcQmsCfMsrqOrM93nR+BHzWbHNZN86YXrwJm3vQERi9BU52a0jvv6dsSqQx3givENFvGu+KOkrldSpaoaIVKoDvuwe7ls1HdcG7HxoyCt6zTaMdts5D1nt9uvthmz8R9nnsRv7bsBYgc/Q7X1+CZ4dL0RGXOmOsV4g5Q94kBIC3PQc+2NiKboMgCOJEXPQceJJKgjhdaAJ/2siidXsrsWbledzaQVyTIZsw282mN5jM7tnbjrpZkCZ29mhjvAC+LytBfacc63mShiTVzYKmAIqSU1UGYGgJvntfzt6De4g3qotlAh4e4HlPPGBZ3nfm6PKfvoPDV0O+4Tt/OnzdmHgj0LydCHi7c+C1SqtVkS527Rn34Mk2Yg6QKwdxYaAc+GmgX/CFg9R48aAJ/GKR1GSh6sN21xmDbZnRnnWWm5zYyee2ycXxTNcZl6orjJoSl8LgcgEA+J981Bm8tmeb99XWAw3IqVeA49Tw6+A/WaPK3YeN75UHclAv6w/u0pruqUDeJMTMxFyQ9GqrkhNFm7MSJiSnrpGf8jpapVWFYYnrsFKjUdo221GO8Yih1DXulBi3E17RxGlCrhzEhWRClZtVWueUA39S/SQuFKTGCwhN4E8b32lygN26t/7147CCHozpN9/v7jziopDUrRkW16utyuo6sz1PBMHLmJFnTwHgZv32PxREFPzlq/q/D6fv/Y4PXOr15ZqtXm3c6TU5lljuuXcEML20Wbn6cYHjG5H6qRTqW52fPTiUofKXLq/c/cWDjSHz98efPR/+QC+/AgD/oH2MnH7jO7JIHuyG/u2ToNYqexRQCvWGWttuhj8P5Eq/qNBbM0EsNguSAz8MuY8ucGvxHOOMoYGb4VQdShDEHMlSuVBAZ5fW+ZgHnVw/CYJ4o9AE/pSR/erFuxsaNgqV5Nj7N2UUfND+XlkLYuNTQfDf/La8J+fcePEF3wpSQ4S7CboPxX42U3MKegC+v159GNinwI9sUoSXSphcglcvOju77sbgdr5y92HrbvpZnLJp54sbQJhv/0EleiS3ZnD59ps9Kr6BVqlrlaiof/YXeLI/mQryJiHiPHuKgR54ZBbVn3kOPPCvvweeiiXJkFjhKdNveNCrWvZQ7C7kt/2vS9NEehDzZ3JXDuoAIhaIZ08xKEkhGSrX7/jAN8+Aa5hLDvynjz08XxrjJjQX/UwPeVZtjVzoLwyD2++ktGfOH571DbxtHAUz8iHI2TsgZvDgv77f5KIt3t3nCTHvdzy92mi1Wq1GtcQAz951owt5HkqNVqvVGruPJVqm5KVarUZJeORP8iQimFQ+it/rDrzqhuF2Y0aTF23yaWPsCYKYL8u3WylGvIjlVBYTn6ly4MGbjvj1Fznwd8IceHj2thySCcnZguD+6lGqPMi9bxim/VKoYEP/kj/B1T/Txgwpq+ssUD/R2/n8ea60SVp05ry+/kvxb+joHxz+TfzlMvUlQSwck6jcbNKqFIo6uFUOpLW21x9zxtz0M5i9s1KjUWLAMrhl1CZ6mSQWmaGKSrP3RYB24BcTd58D6HSk1clAEPxlPQhNVzRhNJpwjp9uBux1m7tOb211VUslKY3Gd2um7WW23s+Cu2t7SSO/GSBHjekgbxJiZlJhxazUqOZNa3wNp1KoN1A2g7Di+Aa7Vmk11HBojKFevE80wu+9BC7hOOgy1W/rfO+x428UlIwhyPBOwwius3TrBJVABEEQwxmvcrNKa6JHHbp+FXxUDjzmq58yW6ReUHwHwHv3St/dmdJ+lCCIyaEJ/GKSUxngAcu364Uh83e88yfptdp449PkVZ/SE9/jnsdtGwDTS8U7hcy/EL5b27a4B1ZqzLk+yt2n7fdTh7xJiDjZOfAAkMdB2bA9yIm3VqnqXL4EerbZLDVakX0FU3PRKyKbpbc9S8x8p2xxVmpsYtu0YznGos4p9krsO909UbKaMSS6l8SLdXu9lLft7vemvNkIauQ5HagDiFhEBmU0JZtJHYxULpYDj5NLK1PVHwCvR+TAz1c/EWWLiJ0g3ynP3JBP+rkgkMAuMlRCf8rk1NFF6r5TK9cc14cQwKoO9H/nIlU/f+09AJkv2FOhVVqNakln8kuP25aZUfbkO2XT4h4rNRL1+cKVJYmIm1eVcaMhYv6eHYdHEMTZwu32uui5qReUoHmzFTThwI7rh2eb+2tiqKp70ZDvlE1bNvqkzokPibGgHnQApVAf2SeUMfHPXOD0nW0bpc2CmvkzIAiCmIKkbE6icrNJa+CA3Gq1Wo31tp0R8zZf/VQK9WSr1ZB3PIIg5gftwJ8yyuo6sz3edH4EfNYMbegZ04s3YXMPOgKjt8DJbg3p/ffUsqY0xhshlINWykJXY0dohYpWqAC+7x7sWjbnI8qe3A8NGQXv2abR1qubleAmcioD/3T3w7b0mWfsRv7bUOqzRwHAd53/WOcAuGV09dJmhbbhT4/JnaKIt4OUiV2qvjNWJOM7TQ49KjQfSG2PGmK0NR1cCpcsthSniTIgkb8hej1ZqSGvqBQ2S23T3nUL01T7DHhEHfU8AL2jzCEFwfS9UVBwwt5Naj85JagDiDgfJGRzvMrNJq3yLHkdWYbfnfJOZ9fPCN8pW3z2fkj65V0USGAXGNqBP22UQlEHPHvLsqPQNHget3Y4knqn3SkxdJvNpH+dON7eFlv18F3hbjJm55rvy/VdoasBbs0wjLK8FBQlp6ps1Aqre1/O3oN74Fa0aqyoeeAoSIkDPO8JP8KyvO/sUcCtmZbdeRFe2DbJ/OTUCeftZAH11iM3cIY72MXlIdp18Z2yYRiGmbJaGroB40fFlgBELJHcCDrqeUm5i1vLTUrKI0o40o0dinbfafHw3DBqtZFWIYnFIi6bk6jcTNIqXInj7285lQ0cNY5Z9TMi8EiaQxwocaaQwC4ytAO/WCQ1Way5DttdZwy2Zdrh11lucmInn9smF8czXWdcqq52p8S4nbgUBrb7AQD+Jx91Bq/t2eZ9tfVAA3LqFeA4Nfw6+E/mqPgjcGUZx3292qpovutsWzZlkJwmqUm7+JI0mhhLzAVJr7YqOaecEJMp8XtdUd+TukZ+yusknJxYqdEobZtt8V47cijafScIgnhTTKhys0rrHKrWZ9HPgNCLnmbvBPFGoQn8aSOC0tite+tfPw4r6MGYfvP97s4jLgpJgzamyuo6sz1PBMHLnNBnTwHgZv32PxREFPzlq/q/D2e6/Y4PXOr1ZRqnXm3c6TU5lljuuXcEML20Wbn6cYHjG5H6qRTqW52fPTiUofKXLq/c/cWDjSHz98efPR/4EADw8isA8A/ax8jpN74TbLOzG/q3T4Jaq8xRETJ3aw2PHomlY0UTFWXkX0oQi41bs3jCQWlEt/qkCLuMubz7aZVWK2hHglvzkC8qWUO+0/bgeYm3ats02vQiuuBQBxBxvshSuVBAZ5fWGX3jUkyrn8FXRuq2ifMOCezCQhP4U0b2qxfvbmjYKFSSY+/flFHwQft7ZS2IjXdriUb4b35b3pNzbrz4gm+VVSmYyysKDtF9KKqtmJpT0APw/fXqw3rYgBXzOfWd8tZhlBT66kVnZ9fdGNz5Vu4+bN1NP4tTNu18cQMI8+0/qESP5NYMLnvvs0cBAN+7WU9/A/IvPSXIaJQIefYUAz3wyAxzi9eCCn3Lj/kewpcjWqATSlJtVbTBHsto6TJDCv7198BTsSQZ4stFTABMv+FBr2rZQ2K1NH1pb7C9kzhlJnl3pNdKYoF49hSDkhSSoXL9jg988wy4hhmkVRRtxiXr08ceni/1kTmfnot+xvozPds0bJrFXyjCaTyVZy4O1AN/yhwFM/IhyNk7IGbw4L++35Q2IEkjeqDf8fSqsHCulhjg2bux9inPk/amY/VTtEzJS7VajdJoj/yBU51t2wseReyiJ1vnw3C7MaOKmo839Udt/QRBnAnLt1spRszecyqLiY8/6S+udqfEwJtBI+Wu7QXuH6keS7EPNSZa0v3Vo1R5kHvfMEz7pXS517/kT3D1z7QxQ0qhHnvgqg4As7swEfMgfF8c+iVBnEsmUbnZpFUpFHVwqxz2qO/1x5wxN/30nS3bA25I/5RqnqyMLhIkxQsI7cAvJu4+B9DpeGLbfSAI/rLekAbwiiaMRhPO8dNlqXvd5q7TW1td1UR85wT4gUnJPLrURSO+l+rEnxnyxpwaMholZkPaHAcb9qzUqOZNa3ztjFKoN1A2Zb16Yq9GJrGHpewZe/9I9IlG+L2XwCUcB12m+m2d7z12/I2CkjGUvMazpwCWbpGjHUEQ82a8ys0qrYkedej6VfCj0UfPTz/x+P/pA8ATyzDCK3HrZ2rjY9JQgngT0AR+McmpDPCA5dv1wpD5O975k/RabbzgPDPnOIFIcOIe9zxu2wCYXireKWT+hfDd2rbFPbBSY04mc0qh3lDFRQEwvbp5p7c9n9k8MQGvr/9y6GIqVUm9hVx7D+i/dy3jiDwOyobtQU68tUpV5/Il0LPNZqnRqoT6wdRc9Io4U01llpj5TtnirNTYxLZp56N7FnVOsVdi3+nuiZLVjCElceV/7oOVHtyd9dWTGnlODrX2EOeVQRlNyWZSWCKVW441OJ5cWpmq/gB4vbI89Cbnq5/pLkvRGPXXs83eST8XCpLixYQm8KdMTmWJ7s84vlPb7qnFOwVNUQr1llozLP47F8B+fP4+/gV7KrRKq7Hm7DZtMXv2uG3x3ug9L9n9lP57oah5oJs8VEi9qgDIHgUARavUW7EO+f2MZHuCIM4ObrdLjVZgqSGbN1tCDnynbJo1NdQPzzb3q8LxyK0ZVjjkO2XThjgtdU58SIyVMWLiH1QM+c7gWMbEf/wCZyKnniAI4oQkZXMSlZtNWkMHZC34LhgVJPfG9BNh1P0mNSARxJuCJvCnjLK6zmyPN50fAZ81Qxt6xvTiTdjcg45gGhs42a0hvf+e8neTxngjprvCMCpua3LUS5qhKFqhohUqgO+7B7uWzUcsMMD90JBR8J5tGm29ulkJbiKnMvBPdz9sS595xm7kvw2lPuHZ4tYMC/dK3eQfgmBfHwC0jMacAAAgAElEQVRjK+9409QRECeGjEaJGCkTu1R9Z6xDx3eaPD7TFSZKMf2Iesi1NR1cCldigizKgET+RvDeF4S5KSKQYtctTFPtM+ARddTzAPSOMoci/XT3+Uinkgmh3pM5QK09xMUhIZvjVW42aZVnyevIMvzulHd6Qv0MCgPYCTI56Rd8sSApXkjIxO60UQpFHfDsLcvmkeWx53Frh6csk7Q7JYZus5n0rxPHh55vgePbmNdNvi9dTUKjUACAWzMMoxzYxylKTlXZqKmze1/O3oN74JYZeKWIZYKjICUO8Lwn/AjL8r4jn7pD8e2f/oPthU/lO2XDMOXsHYDndToIzyXmjzAgGSybf339l+LfmdwVsTDo1QwHu7g8KIV6K9xTNwzDMFNWS0Mzif1eN6FYWiU03DzqeUm5U1bX2aTGmtFdJTyi3FokeRlD4d05gXcocaaMEiISKOL8EZfNSVRuJmkVrsTx97ecOmr/fTQn009olcjFLnxDJM4zJMWLCe3ALxZJTRZrrsN21xlDwvMty01O7ORz2+TieKbrjEvVFf5xKfu49HY/AMD/5KPO4LU927yvth5oQE69Ahynhl+H9yCauWxrS3zd78cWpJWrS5fgvUqd+51Rz0OcgNSknRJBiBMSc0HSq61KThRtzorf68oQouTn+Smvk3ByYqVGo7RttsV7bcaQ4KjnDddAgiCIEzOhys0qrUMXTafjJPoZXuNOiXF7wF6EIIg5QRP408Z3mhxgt+6tf/04rKAHY/rN97s7j7goJA3bmEQ0sQiClzmhz54CwM367X8oiCj4y1f1fx9O3/sdH7jU68ugTr3auNNrciyx3HPvCGB6abNy9eMCxzci9VMp1Lc6P3twKEPlL11eufuLBxtD5u+PP3s+8CEA4OVXAOAftI+R0298J9iEZzf0b58MmkNJLq9UfxHZoHzx/BWusNyxdxT/WZDyE8SpM10OvFuzeMIR44T7LYqaBzC12d2wHOPACBTw2gef5z3ki8r4Ibj3LQ7ANg17jAE+8eah1h7iXJKZA5+lcodBDjxmltZEg+Xnv/Xgz5IDP6N+Brg1w/pvDPCS3UnEeYWkeAGhCfwpI/vVi3c3NGwUKsmx92/KKPig/b2yFsTGu7VEI/w3vy3vyTk3XnzBt8qq1PnlFQWH6D4U1VZMzSnoAfj+evVhYJ8CP+Zz6jvlrcMoKfTVi87Orrsx+NaqpC1GEbiMFjeAMN/+g0rMiK5m8FjvvapXGxVNEcvKP/yrmNG9OLf40d9rsXN3HvEZlZ/8S0dBVqLEZCzfbn38k0kPjteCCn3LjzlD+HJE7ZxCSaqtijbYYxktXY7UApFjnDBadmuGxZk0gBI7WStVbdwQ3JphdS4DP6y2KppbMywDNIc/S+h9kbiAZKhc4rippVUUbcbfnL56OfZu5qSfMRWXpfVLvycrowvGKB0moT4TqAf+lDkKZuRDkLN3QMzgwX99P2jGdPeTjfD9jqdXG61Wq9Wolhjg2bux9inPQ6nRaoV9paMRLVPyUq1WoyQ88id5Et/Ztr3gUfxed0Cqw3A78USFyohwuvHnEgSxkORUFhMfoQkToN0pMfBm0Ei5a3tBz3mqx1K8Ccbsn9K4NcNIenMA8Gt1DrCb0r651wWA3/f87CFRHXWFvZBqlLxJ4nRJmXQM9ewgiHPJJCo3m7QqhaIObpXDHvW9fubxc9TPqI8/6Ix//jxX2qQqyosNCfUZQjvwi4m7zwF0OrIZcyAI/rLekPNhRRNGownn+IwX3iF43eau01tbXdWCWJGx+G7NtL3M1vszg1wxR0JWosTJePYUeNl5XLYfBYHG9WrPsGIV91cYjkUNZ7/jA/gq6uS8DLwrj1IK9a1OYUt2gV66vuWEiqVVWltfh0MAu5cVamlxVmr8tFPeOox9foQXwGVvxzB2AAD6vRJ2RDdmxpC4gW9/D3z/xD8ngiAIyaBsFsaqnHSQn1patUrr9lMjaLBn16/i8GjEbc1ZP2X4sWEE11m6RUmcBPHmoAn8YhI0GS3frheGzN/xzp+k12rjjU+TVy2JBCfucc/jtg2A6aXincKIvXJBEPjGSo0FnL4To3l9/ZdDF0ep6okIufYe0H/vWsYoP/z8x7Ec+N14p6bvlE1bZh8tryg49Hb2i1FYMf+h9FfynfLWoSJO852yuVVzYznw4RB8p2zulJ33x+TAI2oKCmA/jlXd+055giGphnr1YUGBDIOf1c2OGnlOAjX7EOebpIwOyuZIlVuOGhzd/Vmk1a0Ze/1UDvzKcvr+AMxdPwHpXNdeb9TVXcPqfm+WHx0A0s9zAgn12UIl9KdMTh1dpO47tXJNRLophXqrqgP937lI1c9few/AyBfsqdEqrUa1pAdRIx63LbM2uobed8qmxT1WaiTq84UrSxIRNz/eEPUk5xIEMX+4ZcRJCcJADnz09UAcUjKsOGiLETnwxSgHPixVlwnJQeGlUtgssWSD0ATkVAavfRDVvh/1PGmnlDEEoYalrnx4q5tSOYIgiBkZzIHPVLnZpHUwB16f/k5n1k/4zrYNKpsniFOBduBPGWV1ndkebzo/Aj5rhjb0jOnFm7C5Bx2BDVzgZLeG9P57ymdUGuONmO4Kw6i4rYmYHceO0AoVrVABfN892LVsnijHj+F+GLRLebZptPXqZtjXnlMZ+Ke7H7alCz1jN/LfDq8F+BIpx9OUo4tbMyx+mdxP3ghkJUpMQKb9ejoHXjTd+DL4AkiIy8Q58PK/AxluwpZphCKNQGyl29vOal04LSVzjEcMxfyZ6op8IsOe0YmeelJOBDX7EBeMgRz44SoXHjSTtApTo/VUDnx3ylv9/9l7v9A4sjzP9zsXxtBjMV1mq0sPkbGdIbg1ewtBgafGgTwtihi4mBFsjBlSfhA4h2uqGcX2Q3bCIhpnPeRDhRk0C9H50JVqPNRQ0aAFKWBrci/V6C7UwaiuTHiqzTQkhvWCQpCKy6Z7C3cNLj+4Hnwfzom/GRn5R6k/Kf0+FIWVJ0/kCQn9FOf8fr/vd9z4ybfvzUls3+nXfDqgQH2q0Ab+pBFlmnY9UV/ieczcQPxQFUE10uZmRh2nZ6878lpJleC7XN2kjzBeANtzbqmiWisWcsUTa22tpEqAJBVkWQGQuXV27ybFTjxmGqE6ND8mYOEbPO+RB8z21J/6zv0eO3k+V9zRIf+L8ALKX5MTM3A8m23atxOT4vz4wPvOJkOUFguaUDdF5CRODmr2Ic4x59MHfoLbd2JKoEB9utAG/myRDL38WDYru64osM1YNM9Tk+OZfGYbjL9f0TSFib8L6q2ywuzEpZDd9ul/8nHPzhvwbOOu3LqnAgX5LeBZavh18hKutW6y9HsAqNWaxkyWWMb8SDp855JUZORfUmQkzhBnxAc+C7XKG0QBAK4VNyvOHnI7PaG2ICsY186SIAgig7woFwbQ8UNroj5zbEaNn76z68HzEqcStqHvTiSYEwTRC/XAnzS+s8kA5eZqWVOU8FVF0VZvKgg6QV2LN55KC4sKAiN4Xa84Phc0BW40lmcv8bkzc1o93L532z7wu07XqYjmVd93NhmuKDynrmjlZuP9b3xexA6uBn01uBJwaWZ+NfvPys6D59k39O3XAOA/3H2GgnYtvCXlmlbAs6hZyrV0w2TQalqWnopabda0eNr/2l+RPh5BnDxPn6CnB763DT5Or1nxIAIf+HCWUwk+oafHMop8Ofzr78OIFuKLGKjresX6byxeo5Q9VJAV4H/8+j+EQ45/2PGoled0eH31l/HDytSXBHGmefoEvSEpJCfKddvR09nooVVaWFSijnQA+GLHg9/O95KbSPwUD6tpEoshzh8UqE8RysCfMKJffeXOkoqlUjU59u4NYQUftL9Xrwe28a6VaIR/+WVlu/uK//vFPqtXZLHtnp2X8BgHn/KWKUUuSOgAeGOx9mmgfwo/0jnlYqhRdH/1or2x5S71pvOlO5+27qTvxakYdnFlCQj97T+sRrfkWjqLZa9krdasqhJcCyxD5VoCDnmlGCzdZJeH+WZmcz70S0nekzg9Zpdb928P88aCrIDZW26pqmJEH3hmB8Xp3Ad+LfSBj/VYCofkfDci9x8/ew7EDwbdu7rZxlvlZqskwbdWDIa5VXXAkFR6b9befvzsar1Vvwq4H+mGiUGfTRwrYzwLksAHcdbJiXLhw9h4oZVf2QweCLkP/KX8OROLn3ETYlH/n2gKJc4tA4MtheXjgDbwJ8xhp3+/uti9A8EO/p/uHrR5BEzt39Fte2I/7LvOuml7YaAHAM8Dj6/AgMIrrnkSqtH5TsWw+0nYpac667YHbUUFRFNXKlWVNLdTU6cVKVzLZMKXbjTBaYIgTo2UWbFSbtaKhjm4hlMqNZqoGIFZcbzMUq22mnI4NEBQL94nGuF3vgUu4VnQZaota2x7x/GXSlLekPNVF4Wrbz2uB07GM8APKP8+NVDPETEtDI5y44bWRI86NG0OLCcHPsH4mbzG0ycArtwkQfoLD4Xl44M28GcTd48BaLeFfl2PEfyM1hQC8JLK43xi262N1EDuHWxuOZ3rCwtq8gw1B9+1DNvLbb0fBbF9n0ysPyfqlyTvSZwG+T7wAFDEw4puexAbb6FgAQDwbGOz3GxVw19kRS5Ej4jKOO2QeRXsvlMxmVJurmHdsGM+xvyYNPZI7DsH29zoYuDQX378cT26vGFH9hgEQRBD0RtGU2EzGVKiKBfzgcfRQ6siyz8EXvfxgZ9s/JQSV/6qC6V87w5FToI4NmgDfzYpyArgAbPLjVLG/h2X30xLLcWVS4bv2hSa+B7zPGbbABStvHKrlHvEy7XoPIiE+ZEJ/orQU3IckvckzijM3g2c1sBTOAdhwY/vVAzDksPnO8829mpc8ci1dDMc8p2KYYs6odSc+BAfq6DPxj84cPSd3rGcIDje0Gicj0aeKYJ6jogzTTJsDhPlxgutrqWbTGyx+acgqzcdOI74Gax8y/aO1nxE8fN8QGH5WCERuxOmICsJ+aY4vmNVLMf1wSNrTQO6/8vl6fhIzeTtdwDkZshGQq22mrWyFoR4j9mmkSNY5TsVw2SeUm624n9tuKxqEm43P9DRhKsCeLYRyKKYDFxFa5ByFUEQx0JKxC4VEGIFPr6zyeJfSwuLSiK+RT2Q6nWNHzMieMAT06TSihbId/LOnMjLTSqtlRXP3hqtr6ZHI+qw4wk5pfGGCIIgjkQibA6OcuOFVj4rqIyUSo2aNvpKjxok3T020NiYIIijQhn4E0ZaWFRsj2067wMPNneZx4VJFEVbuQGbedAQ9IoHSnbXkc6/p4xChDBen60yV3yOWyHxnXXsHWqpqpaqgO+7D7dMm/Xrgnc/0oUVvGcb+m7YOg+huPLF1ke77BGXz1OuFb/LPqv9Hbji6cDw/t2gN5xzeLKdxD+IEye37zz+Sx013fhOxQhklorRmzNP8PzOQeIBT622WuKfhx0vGe24l+ZwuhzRqhIaUa4VtXmONzQ61Ody0lDPEXGWiYfNnCgXvmms0MpFjRZjT10FWcHBiEs9WpD0nU12ZPE6+p09J1BYPk5oA3/SiKJ1u54oH/E8Zm4gpdmp3iorbHdz0+t1ZvfsdUdeKwkRO7u/MF4A2xOKz75TiYVc19JNppRrayVVAiSpIMsKgMwSKfeu2L0Ha2CmcRC0XvFjAha+wfMeecBsj6O879xP2clLpUarFP8TJVDKF1r+mfbtxFQQU0HSaq1qgRdtjovfOeDHg6lrFEe8TkLJSSk3m+V1Y5eHtfGGiDMO9RwR08KQUW7c0Dq47HEgRwmSPacTxMWFwvKxQhv4s0Uy9PJj2azsuqLANmPRPE9NjmfymW0w/n5F0xQm/i5wO6fEpZAdfP1PPm73vAjPNu7KrXsqUJDfAp6lhl8nL+Fa6yZLvwcQ58ZB1k/0cI0ud3VOINFOYmpwLZMlFJSO2PXCe3HGEbvrQa3yBlEAgGt5KK5IRxoiCII4OnlRLgyg44fWwUr1wzBukOT180c/QyAIYgDUA3/S+M4mA5Sbq2VNibRFFEVbvakg6AR1Ld54Ki0sKgiM4EVT+NMnAHCjsTwr3D1n5rR6uH3vtn3gd52uUxHNq77vbDJcUfgJqaKVm433v/F5ETsAqdSoX50NfUIvzcyvZv9Z2XnwPPuGvv0aAPyHu89Q0K6Ft6Rc0wp4FjVLuZZumAxaTcsQRD3seJOTjyIIYmyePkFPD3xvG3ycSJ8jaOcZBK/Xido5facSfEJPj2UU+bLwnYqu64btAcxMrdG1ouX/A0vUKI03RJxtXl/9ZfygM/XlHzz+W/7faSyNuGA8fYLwIauXnCjXbUdPZ6OHVmlhUUl2pH+x48Fvd/tPAYB//X3vYn3xDKnresX6b4lImDOEz/9zoGGU+0eDuDCk4jAxQSgDf8KIfvWVO0sqllLW6P67N4QVfND+Xr0e2ManjOBfflnZ7r7i/36xz+oVWWy7Z+clPMbBp7weXZELEjoA3lisfRron8KPGZX4TqX+OIrur160N7bcpd50vnTn09ad9L04FcMuriwBob/9h9XollxLZ7Hee1kY17sWWMqmyu8cAEUcWhWDeQAU7SgNVFOtX0qincQZYHa5df/2MG8syAqYveWWhLb8uj3E/j2o/NkUbT3ulu0p5TUV6Omx5HmoPnrGvPEmsEF6CWbq4GU8oviUF/X4zo8Nu4sf8knjDRFTQ+/DItU0EWeLnCgXPoyNF1r5lc3ggdC1trvApfw57j9+9hyIJ1bcu7rZxlvCJcRaMRjmVtVBQ4Br6Rv7gamda+lhQCaIBBSEJwJt4E+Yw07/fnWxeweCHfw/3T1o87b41P4d3bYn9sO+66ybthcGegDwPITmI/mFV1zzJFSj852KYfeTsEtPddZtD9qKCogteCqHnjS3U1OnFXG4kKkd7k89ZhusQ5GfIM44UqlR6+imqYd2xLWiYQ6u4ZRKjSYqhugCTbgYq9VWUw6HcgT1hFdRoyT5DoB3Vst/tCHU7gpyATgEE+vSarV3THPT8dWSNN7Qkb9RBEEQAYOj3LihNdGjDk2bA8vx0Yj12Uf4nW+BS3gWdOlryxrb3nH8pZKUM8SrS69cgajVTJ7SEgQxaWgDfzZx9xiAdluogfQYwc9oTSEAL6k8zie23THzkSHwDja3nM71hQU1Ej4dgO9ahu3ltt4Pj985ABBK6cF3LcNkbNzIP93iliTaSZwqb78DdHNtKot4WNFtD2LjrVZrGhMPgZ5tbJabrWr4a6vIhegRURmnt71va01Myp7HLd+piBNDPNw9TH6a7yhguw/90sJYQ6PHoamuAzpPUE0TcQr0htFU2ExGlCjKzcbqI48eWhVZ/iHwej6jbxFC0TioYCpGi+VpptiRgu8cbPNAmDPEP/6Nf9/8dAIbdoqf5wkKwscE9cCfTQoybyafXW6UpJQRPABcflNKv1s4LAM5z7w9SKUVDfA8ZtumYei6HhjR5+C7VsXg+iqTSZFLpUar1WqUgoNlSa3WNKR6xC4I/WqKqNaIOCswe3ex2Wq1Wq1GSYJr6eZBmX/dajXLsI1Y66NnG3vX+VBN86Ih36kYNvi01Jz4EB/L7oCXSo1WImvF7TFD9aSEICiXjcKRhgiCIMYkGTaHiXLjhVYhAsznLO7aOV6YUqnBV5NBzkNkvyGptKIhsrN3t2wv9dxKEMQEma4MvO86W0nv9MiI/Dg+TsTY4bJGGU5oANLLLMgK0KdI3Xes9Y68cqukSlKp0ZIt3WT/ywWwF8+/98uQvfzS0G1ttZy9un/dqeifiXvhQnd7vBRLrbaa152tTZt5AOAx2+xfve67n9z9+8+ei+b7zqEPUfUvyUXg4F9/G3SxQ1G0936Q1M/nIvT8O/TP/+T6ap8fnWvpJpvLPjImCOL4CarIBan6zliBj+9sJlrUe1zbI2NM9boGJipARfU7n8bbNkXBje+s255SboorSqW18q6RaBDKhttjik/jW/me93idQ8hjDWHkvzJUL3NWoJom4kyQCJuDo9x4oVXMEtcRZfgHI660IKcqjw47HoDOYe6QFDYG6LpYYNjIOTL0u3muoCB8PExPBt53Krph2sHuHdw73aiceaFLz2NmdHDKdeWF2HwSd8tmHuuEz4rqdQ1ge25v/j2Rbg+E8X7wR9mfL8lFwPv/vole+Z/BDjx4h1qqNlqtVqvZrJU1JPShEwu8a5ifRXM9ZkanxgVZgffZBgt+Op7Hth/Fz2pdyzDDUbxom/FkXExm2rVMBuDb7kTsTKeRfC3lHEhmmZgQIoETkNw8xxMwURpcKBOnDzEzf4lj1e8AuC2ROCY97KSyNtLCotIvIoUEHT0X1nuS6AfVNBFngnjYHCbKjRVauahRPEMeFHOOgkilr4uHMvFMNmiIS+kbQZ1Bq7m4a5ASPQEKwsfGtGTg+QMaFK22dkvlWV/fd7bWbcZM6/rZUTvrlSLheeeooZunm5htVDo99xI/VIUQAdnd3PR6ndk9e92R10pCxM72AO3/+AHLMGoPeHTwe+ANAL5T2fAA/Nl1rn6qmyzsPpekgiwrALJKpHyr0QYuXV1t1JckpMXzJH58cOnq8t367at4/KvKvW3v1aUrc0JJj5vnlWtrJWzpJlPeghd8T/jJsr1uyWu3Ouvi70G3r9TfBWGk0EYyy8RpEVNB0mqtasGpGPb4V/M7B4Bnh9pOAcWcKYEWfbR77/PQqsiFMYcIgiAmxJBRbtzQOoHMR0IJTyk3m+V1Y5cHwr5DvrPJoJTXgjAs0v+kYkcQx8R0bOCD7V+imF2SStU1HBi22AhmyWlG++lY/XZv6X00qGjltWpPtHGtSt5wLpJaXdFYvIhJrda1f64zj5lGar3actE2Kmg25C3xgVdm8DwsROcl/do1AIoC24xFc61WfXuPX+0l0LUN3Q7F5bmivfcceM5fv6ZceuT9854PVeJCoYlLofe4AAAe3/9/XwB49XjD0Ddir7PNz6vqEvyXAIBXj7fr+nY4+Or5vo+rkjgXLq+VVAkuABT/uowNW1RiiTON5Pdj9ihOcgRBnASuxdUwwsh8RNkK3nU+vNgdj/qZEk4JvWbuVBk82I43REwp/CgzPOWkk03idMmLcmEAHT+0DlaqHwa12mqFzkGu5aG4IuUOuZ1kzyREwf1YLUjEOYOC8HEwFRv4wDu9N9pJpbXmQsx9rd8FEu3pnsdM4yAKjK5lhDt/j9nGARKBtbNVsYPab4/ZBuRREv6+726ZDIhyO75TqbMXqbcVFO1v1qrqocWSH/j8BQBc68lFF1caK3tikz+r1epVFe4egIP/+7+GPqLhbarVZvnbu/aj50BBK9eqc/dLj4LvhlRauWabj2KXfuvmWtaflf1U3X3I5X8DiE6oa8va77bF0hVt+Qds+xEP3oc9of3fyEVEoV2ttpr4scH42meAF9c+GPvQ9qLpl5LCJzFBnj5BTw88cszckKgF5bG6OOAzJLmYUALxnYphF2utqtrbYxlIOWU87PK+d4TJrHCNvKjn8S90OyhKmp8HlMXCEEM8JvlOxeis1ODxIeLsMeqzID0yEifH0ycAnjwFMoNmTpTrtn3g5VPgbYwRWuMxjPPFjofnV7rIfZ7619/3Ljb21Kxo1zxoNTV/qCArwP/49X/QTW5bp5SbKx0KoBeGYQIyBeHJMhU98MN5p6vVqGezpgFhPTo3LFe0WtCaUysrkVZmmN3nQz3q5x5jkVZoToM4h5l6EoMfDoSnD5mLAf73lbAkwGPME8PNmjYDAC+zPkqtNlrNsgK8cz06bfUO3ygHc8sKgtuUSv/Xv1cApVyrliTMzkvAD+Tg9h8lb//Zb7PE37lSfJxmWQGg1e6pCHzg/91fVMN3Nap/8e8CbfyES7xabbVaVTWlnO9usa5YRe3PAFzu/z0mCOJ4mV1O/bb3270X5FgsFdFtCNRbZSVSAnG3bC9oHkr1WPI8VKYtpu+YgSmTiPnMDDoupdJ7s2i3g9tYnm23Mfue6GHKGVrRwEzL5R1bv/unfh9NnCoppQ8S/iCmjGGi3HihVcSwStijvt0dMANw//Gz56lX7uq6YX8rHgu137FHmPsTdcCQVHpvFoePn12t84fwH9gGBdALAQXk02IqMvAjIvIygaJRUL5djVzKuObnnltV1bC4W5iqx2uDOJEyklS6rtksx5Uji2TBfv5ixIyy8Hj3D/cOXmAGaO+5yFCtz/q0svBSDy/cR/EeyfX0v/0+8ybpA8//fkWarEfkwslaksIncVqIPscgYa+Um7WiYQ6u4ZRKjSYqhugCTZTACyXjsEG0T+7ff7jDEz2JXlLWsG5tViXf+aqL+fnuti4aeubn0f7K8W+X8oYAtdpafqKbvG6l3c4pOyAIghiXwVFu3NCa6FGHps2BHfZ/d1bnKfzOt8AlPAsiq7asse0dx18qSXlDzlddFK6+9bguVOgxA/yAFEQI4rg4fxt4vrOMeZRzm4tewRCg44uSobwux9EUPdNN99AWE+32+YuRwtVEYbVwtXD4eNimpqRYvbSwqNje4LkjNnkGigGT8oH3ncoEt+8Xj9dXf5l53knVSsQY9LOpjCjiYUUPkt+NklqtaUxEK882NsvNVjX8VVbkQhTLMlvVB9HXdHjhhmKLunsO1wiREZ5L/qRxT4qP7T70Swv9h7g783ZXKTfXsG7YxaMcT160Rp4TgzqGiCmgN4ymwmYyDkZRbnZewuMin3j00KrI8g+B1/PZrrzBoxcPd9Fiec1rIrQebPOS/4FDf/nxx/Xo8mFkHRWKn9MCBeRTZCo28EN6pwOBq8VZsBOS1GqjBt3s6akfioKsAB4UrbYmbxqPj2eJ4yC6n9J/L7gPfPKtscb3vFHf2c041GCmzsZ63icI4lhh9m652WqI30zRvCkMf32nYhhWJBPi2cZejZf1uJZuhkN8s82npebEh/hYJYcPY20AACAASURBVDOCSqVGqxR/IQo4PpA6l0wGqL5DwTV9Z/xvD0EQRJpk2Bwmyo0XWl1LN5nYYvNPQb+0U164yzHfGG+IIIgJMxUbeJ5JZpvO+8CDzd3ACl5RtJUbsJkHDVUgqJ1PJ4YLsgIU+9VC+kBf1c5u2wfQHqD/kXi31yP+BHh2/ZOF+3ekwYvhHHR8qMHDqe9Uwm0wl37C/9lqfSjeetiJZE2ePgG6O1+gdDu8Ny7+J0upe0nf19Cipe5HutC782xD3xUq9whujH2x9dEueySk/q8Vvwvjef5oP74bYkkEQAqfxIRJidil6jtjbY2+s8mg1cKvuYhSoiEocJNQr2tgIta4W7YXTuMuFMJtyHfWbS+qx5F4I5Awq8whCP63VCTjYgyvcwi5/9BEpZKpe+W4oI4hYipJhM3BUW680CpmieuIMvyDEVfao7HHK0c7h+MOjR5Z6Xd5aqCAfHpMhYgdF+aAZ9dNO9i9g6vJb0Te6UFXdjpXIy0sKmBmxXEDwTTfdSq6zvWOpIVFJaYm4juWruuBAsik6H5W/9UwixH3Za+L4dDjPS7gx/bCtVZ6upe628HaM+f2MMrtu3cTavXwmGmEb5XkInDIQnV7z3vEDjErSvrzRqVSI1DES6CUa5R+H4nXV38Z7uRJRIQ4AlotR8Eufu4mlRqtMKeu67pupKSWMvtz/M5BIjKp1VZLxO3DjpfsBOIhM085FP2DP3Hu6Hc6SaeWxJkmHjaHiXJjhVbePDRS22cGKY09Xtd6lCHi/EIB+RSZigz8AIqyFIjJZ/gfabVWdUWzTdZjdh6pHtsmi1dwH0E3c3a5df924hX/859WNva725XPbzeWoo/LXgz/otfjPX7QyphtMD6qaJqSFNWbnU/WomeqzM3OS3gM3hY19O37n3zc7n3Vs427cuueChTkt4BnqeHXwT/yR/mfnSDNJ0rA6Fl8VFKbdv4lhVHiWImpIGm1VrXAizbHxe8cpGTpOMWcKU7FsL1Ev02fh1ZFLuQNEeeUD3bwDzdOexEEETBklBs3tI4oapRFQglPKTeb5XVjl8fI8YYIgpg407GBF1ZvN1cXv9kJK+ihKNqNdw82PmObzi11IdtrjaNWW3X8+B7rcivzSzPzd/7+3lKkelxffnJvmw/OKKt/37PndS1huV6YuzLq2qWln9/5f/SNfW/Dcpeqav5inj4BgBuN5f9S4uuZmdP+Y0wuZJPhilJ47h0CilZeq87dLzG8jPl3Xv5RY/l3Yu6lueW7mXWnyRL6wbcPAP7Og+cZLwP49msA8B/uPkNBu/aHQZpduaZ99ygoqMofDQ6kKdATxGkzmg+8a/GupXDzfMTaJUkuAhhe/OLzir7hAfxxuJNY48Fe2PEDXNOU2INtzlDM4tgzreukQn8GGalj6IMd8X/awxMnR64PfF6Uexz4wGPs0Jroifztlx78vj7wOeGOp4sAwLONX8wDxRXpSEPEeYVaOE+LqdjAi17ulTtLKpZKSZsz/90b3Aq+9IFm95TsRFIedRbZYb560d5Yd94NAqNr1SOvzBdeNMQz1d9/IHbvAA73n/etSY+rh8bW57tbG/uIdebnLwbAyy8rYjsNvNhn9YocD/RvLNY+DYRQ4Mc+8+13gO7rnWjuq/3teuV7zfi98Kx7/N85t59AuvNp60763pyKYRdXloBAQOr6h9Xox+NaOmO8ASp/lB9IF3FoVQzmAVC0eEXCqFxM/VLSAiUmR28lUQ7xWlAeq4sDZnA1j6idk0eSWquq9jZSBlJOGUHpcb204WFGa25WuXGHqaPWqqqQFhZnbZs9ShT1YHaxgLyhWDJ/DeuG/RIsuCBx9hj1MZH28MRZISfKJd43cmjljfLxtvOvv+375rxwF5fCg3tXN9uzy2q01NGHiPPOMAGZNvmTZSp64MX2LzMQ8N07wPsoA2oaEBam+8667UHRak0+2KyVFXj2Fu83Etn9cpMPaYC3+zB21ukxBj7YapY1IL8dk5l6EsMwGWJl6fmLAQB0254YbtbKClKj+XgeyiPNHXT7eTPXbS/4sfidg54y1IKs8MPgAaPiB8zs8JzEY7YRVwUgCOJMUpDj4YvHhCFQb5UVsM2gW3LL9uItTbFGSp7iz+zq8a3/9PgV5lY3ubOSWm2WY22kr3veH77Ud0hI6wVHBe+slgd33xNnGZ5+J4izxTBRbrzQKpVWNDCzEjaiR9mZNDnh7vOKyTC7zPfyvrPZxgy6Xzn+2EPERSclzEQ6TRNhKjLwIyLE3YLIJDyBI8F0SeWan3tuVVWDUaGHrFa5L0eMKMJJpeuazdJZ/gEoirYSfXb+YvhLM1pTDEsqFxHtY6GX9WnltVI4d628a9j5cwfffp95gWrUJLJTfucAgFKuibX7rmWYjAlZ6pG5oNKXpAVKHD9PnwDftncq9meBoXGj1tHNWMX9Wwqe8RpO3qrzddTJOQP8QLxLKjXq7VJddIFeulp3SrGT2Po34RCgrGbmwP2df3kBYH9D1zfiL//6cVWdfbj7DIp2LeoEuKYpvGVnIWcoJa3HGdqjgzhj9O7eKQlPnAq9YbM0MMoJBfmRQ6tabS0/0YMGe+XqHB4fZq0prSTKOej4UPESwKXutq5vAwC0WnNxk1u6L4w1RHpGBHEMnL8NPN9ZxrzkuJdFr2AI0PFFg1Ke6Mdoip5Rp6jvWusmg7YYbdYHLoa/7/Kb6XPYoR8iE8Kmwn5v8NwRNU/4nXk9fn1jkzZ0ltRqTWMmo8g/Aq+v/jLzRJNKlYiRePsdoPtOuhUoPsoe//avYz7wW/FOTd+pGLbwPpqdl/DY29hbicyK2Z/JQSVSpf5Y4tN8p2LULTfmAx8OwXcqxkYlq6unt6nHtXST/flfXg184K9/2Ei27BwEX2QPxeIQ/6dr6YEN58hczEaeM0bGweUHO/i7P6UfDXHMJMNob9jsG+VirZju3jih1bX07W7KBz7ol4yTE+58APjzRPOQuwccBF+MNzQSFD/PE9TgeUxMRQl9QVb6Vq77jlWxIlM2bl1xFiTMJbXaqGkes40RTOnefgdA34fns4DvVAyTeUq52Yp/j7kqSxJuxixLg0azGMf7hCCIyZBqBUq1s/T4wEdf99ghJc2Kg6YZXr25EvnAh/X0wiF5LeaQrAzTRNTrA9+L1znMG8q5IDFt/Ow3VHZEnCl6feBzo9x4obXXB14banETiJ9Dh1aCICbCVGTgeSaZbTrvAw82Qxl6RdFWbsBmHjRUgSgGJRPDBVkBiv2kiHygb4I7qdY+iG7bB7we9WbAs+ufLNy/Iw1eDCexGiHgJ0sIpJ/iAiV8F8z//fQJ0N35AqVQfCo+N34v6fsaukjUDbWbPdvQd7VaVF1QkBWwL7Y+2hU684pyrfhdWL+QO8pPiW+uLn6zYzMxPH/ZGzv3dWEhLVBiQvTRnOekfeB5GiemZxxXWhraB17887DjQSmnHJJte0AT0cR94I98QepbObP87DdvUiE9cdL0+MBnR7nwTWOFVt4TuZiqGj0YtLaJx88jQ/HzXEENnsfDVGTgeYoGnl037dBEDvA8Zm6w8CCyXwziB5dmJUrT+65TCdJK0sKiElMT8R1L1/URcuZD0f2s/qthFiPuy14Xw77LlUsSjUpsL1xrpUd2v7sdrD1zbg+j3L57N3Re4utkZlRdIMlF4DBwiQM87xE7xKwo6c8d5Wv4bMNm0XC7Dcznrpzow+urv+T/5byHK4iQiAhxdFxLyHXanlZrtZrlI9XOhA7JMR3QAeJNvlPRTaaUm9GZw9F84DMuSEwVA7XrMt9AinfEyTBklBs3tI7WEzmx+DlEaCUuJv0eRynJdESmIgM/gKIsBWrqGQbGWq1VXdFsk9mmEW88j6se2yaLt6Vnah4PR6/9kv/5Tysb+93tyue3G0vRx2Uvhn+hIDEcKcVxj01mG4yPKpqmJEX1ZueTDfaZKnNxG7mhb9//5ON276uebdyVW/dUoCC/BTxLDYd6z7mjkny598rA70k9avKkNu38SwqjxJictg88l3FSsiYkK5k6B3Ef+P5DORckpoUxEuxkF0+cGHlRLgyg44fWEXQ3Jx4/c4YIgpgs05GBF15nN1fLmhId8imKtnpTQdA9+bL/fLXaqmuzl4IvL83Mr8YCllqtL4eDM8pqrzSba1X4Uaj55e9HXbu09PM7cwC8DZFkz1vM0ycAcKMRrmdmTquHy/GdTYYrCj/PVLRys/H+Nz7w5Gn0aZd/FM29NLdcz0whdds+4Le7w94+APg7D55n3+C3XwOA/3D3GQratfDHo1zTCngmLOnyR929NlC4Go4qSrnWbLU+pidogjhpnj5Bhh1mnqtjr1nxIAIf+HCWUwk+oSArKSdL1+pXFBTWIIlkVrhGXtTz+BfR8n/x2OOpoJyh9AUnXYhFEMQF4ekTJB/NEuREuW479kw3cmjl4S3edf7Fjhc97CWZcPyMf7bvVHTLPex4lIC/2ITFnqmy0IFVosQwTEUGXvRyr9xZUrFUStqc+e/e4FbwpQ80u6eiPNLirLMohL160d5Yj4SNXaseeWW+8KIhnqn+/oNKaFB+uP+8b016TD00vj7f3drYR6wzP38xAF5+WdnuvhLL2Wf1ihw/Hn1jsfZpIzzhjH3m2+8A3dc70dxX+9v1yvea8XvhWff4v3NuP0Gv5rNozSquLAGiG1+7/mE1Ke4sOvZzR909Bmh/k33YMAakX9oP0gIlhqO3kqgPBVkBs7fcUlXFiD7wzA6MIrkP/FqsIspedxZ4FOIOybWsVJVj2qEpkwTX0k1TB4/4Uum9WXu7HdzGr36sb3dnl0sS8oZ8p257wLVa60Pek6Ubeidfr4Q4F8SL5ykJTxw7OVEufBYbL7TyK5vBQyP3gb+U9c6Jx8/gs63rNZi2h/l/MtvZoZu4AFCx5wkwFRt4sf3LfI7iu3eACyEF+8OgMOhWFPqUUHLNd5110w4io8juC0Mj1zJS9mUeY0q52eIPeJZhs1xj9Z4KfkFYlp6/GABAt+1ptWZVlcSglxjNx/NQrjVLw88ddPt5M9dtD9qKCohaqdRZa2SAh/xRAIpc8F1na5P3wSuKtpJw3yMI4iySMitWys1a0TAH13BKpUYTFSMwKy4nKqJaTTkc6iuo5z/c4XkmL97/wxrWrc2q5DtfdTE/H3kSz8+j/ZXj3y71H1rY+a9dAHhk6nr4Kcz8sdy8T8+g5xhqfSdOnsFRbtzQqlZbNeimuLKmzYFlycBPPH6WpMCD3uTJgHY7VwuVIIgjMhUb+BERhUGBnB3X5SxHG0JJLa2Vdw0ubByMCkOj+CmAIJLFk0rXNZtlbtD7k9yM5i+GvzSjNcWwpPIYnntmkPy08lopnLtW3jXs/LmDb7/PvEAy8Ojx2e8cANhdN5LyhMbBuG2oJGvZF9ICJQaR7wMPAEU8rOhR8kat1jQmip8829gsN1vV8PdWkQv8NBXAeJ3l/UowpYUbim3HHT24nYWMMKz9pHFPio/tPvRLC/2H0kVGvMLog/F271QHND2kQx95xRNHpTeMpsJmMqpEUS5eyXn00KrI8g+B1xk+8JOPnyUJvlPZ7irl5hrWDbt4lKdDip9TDRV7ngznbwPPd5YxLznuTpmQdhME6d9cmY3E8+NAe/LoyNF3rXWTQVtMpJLzF8Pfd/nN+GqiTPUQz5HxlqnAfm/w3BFVRvidJb7HR8bzIKoOog8YofCAGIrXV3+ZqTxPdU3ECDB7t9xsBX08rqWbB6JGiT/NGZYcPhV6trFX46eCrqWb4RB/WOTTUnPiQ3ysgqyn08hmScArtWRJyD0lwpokFxG5KeUMhQRWzRSBzjNkF0+cEMmwOUyUGy+0upZusqh51LCBzKfW44ifwTV9Z8RvDkEQozMVG/iCrAB9Msm+Y6135JVbIu/sWvHk+2kiqdVGDbppGweZj5+ZDM5+nTbCkjR94Jv1EBz9PUDeqFBkjZ1zSGp1rXxg2COIqRIEMTlSrUCpUsiYT4XvbCZa1Htc2yODDfW6BiZ+p90t2wun8dZJ0RAv9s3NsOpprbxrDHGWx+uuxKcddryEY7LA6xxC7j+E4CaCxFa0jNGhkpazT07xPHnFE5MmETYHR7nxQquYJa4jyvAPBi9ukvFzElD8nG6o2PNEmIoNPM8ks03nfeDB5m5gBa8o2soN2MyDhioQxaBkYrggK0CxXzOOD/RNcHfbPoB2F0M9xXXbPuBl9MB7dv2Thft3pMGL4SSNOLiAnywh0G6OR8p4oH36BOjufIFSKD4Vnxu/l/R9Db1Rdj/ShRW8Zxv6btjIL26MfbH10a4we1eUa8XvwvqF3NE//OZSsg8r4NKw33piaHiyPczDU+6dyCK3eTFelBSlccTRHoDEA15meY/fOUiomqjVVkv887DjQSnHCol6nluzCDp6JnNwKzqJXEs39F0ylCMI4sjEw2ZOlAvfNFZo5cXvi6mq0YNBa5ts/CQuPFTseTJMh42cVFrRAM+umzZLdkpvsPAgsl8MkhYWFTCz4riBaYfvOpXANINbX9jrwjHIdyx98v5B3c/qvxpmMeK+7HUx7LtcejQh4Mf2wrVWemT3u9vB2jPn9jDK7bt3xe49WCczjfCtklwEDsX+HIDnPWKHmBUl/bmjs/820wYeuPxvezu3iBEJbTziL3IPDwqmxNFxLe4mZNieVmu1muUBXUb5+J2D0NVIDy+cP8Wp6CZTys3ozKFPq5MiF/KG0qi3ymmzJ2JaGChNd/Q3EMR4DBnlxg2to/VEHlv8JAjieJmKDPwAirIUqKlnyMBrtVZ1RbNNZptGPMcb1B/x+k0WzwDHSp1Gpdd+yf/8p5WN/e525fPbjaXo47IXw79QkBiOlOLU6xoYY7bB+KiiaUpSVG92Ptlgn6kyF7eRG/r2/U8+bve+6tnGXbl1TwUK8lvAs9Tw6+AfeaPSnU+bf1zp+QM2o927Q8fBR4BsPIhjx7V40VN4bHrE3a5oqBk66x34jWRMSFYydQ5iD7Y5Qz2LYZMuDiWOHb73zjeEowp54rTIi3JhAB0/tI7Qe3is8ZO4yFCx5wkwHRl44XV2c7WsKdH5n6JoqzcVgG06PoCX/eer1VZdmw3NMC/NzK8mbIvqy+HgjLLaK83mWhV+FGp++ftR1y4t/fzOHABvQyTZ8xbz9AkA3GiE65mZ0+oxldBNhisKP+pUtHKz8f43PvDkafRpl38Uzb00t5ztrd5t+4Df7g57+wDg7zx4nn2D334NAP7D3WcoaNfCH49yTSvgmUhf5Y9yU6la/GeLQvnvq/QngSBOmqdPADBTTxErEUoTl87kbTuD4DvjvfCSvlMJPqEgp7PertWvKCisQRLJrHCNvLDo8S+i5f/iscezRDlDsVWIj/34Xyi3RBDEyDx9guSjWYKcKNdtx57pRg6tPLx1Yr5xX+x40cNekgnHz+CaQT0BM3P+aBDnmrDwk4o9j5WpyMCLXu6VO0sqlkpJmzP/3RvcCr70gWb3VJRHWpx1FoWwVy/aG+vOu8Gu2bXq2+HgCy8a4pnq7z+omExEzsP9531r0uP2H7H1+e7Wxj5infn5iwHw8svKdveVWM4+q1fk+PHoG4u1Txvh4WfsM99+B+i+3onmvtrfrle+14zfC8+6x/+dc/sJpLTHEgKbpZUlINCku/5hNfrxuJbORPoqf5R/gFptqFVxJKyUa+M3Y5EBCcjGgzgSvZVEfSjICliovuTztp3BqLfKCrOFbh3crUjvnZcE2evOAo9CrmUmpJwifMeMmzK5lm6aOnjEl0rvzdrb7eA2fvVjfbs7u1ySkDsU67bnaqh4Xijfo7bQ6SJe+p6fhCeI0yEnyoXPYuOFVn5lM3hodK3tLnAp652Tj5+iWT+wkXsJFl6QuChQ4edJMhUbeLH9ywwDfPcOhMpDAKLCoFtR6FNCyTXfddZNO4iMIrvPg5jvWobJhKclx2NMCc0+LMNmucbqPRX8grAsPX8xAIBu2xOWanxwFD81z0O51iwNP3fQ7efNXLc9aCsqIMqoUrmqyAAPuaPxj+LLOUIPA0EQJ4eQOQ7CnlJu1oqGObiGUyo1mqgYonMnUcKpVltNORzqK6jnP9zheaaEACZrWLc2q5LvfNXF/Hx3W9e3AQDz82h/5fi3S3lDUqnRki1d14OrXbl5hINE4kxAe3jiDDI4yo0bWtVqqwbdFFfWtDmww4y3HUP8FN4ijZLkOwDeWS3/0cYg+VGCIMZmKjbwIyIKgwI5O67LGbMpk1Ru2rHnVlU1GBWGRvFTAEEkiyeVrms2y9yg90dRtJXos/MXw1+a0QJDdEnlMTz3zCD5aeW1Ujh3rbxr2PlzB99+n3mBZOBkT1fdLdtLCgKMAdlUAGTjQYzJYCvLIh5W9Ch5o1ZrGhPFT55tbJabraj7RZELgSkbslstB9GvhF1auKHYdtzRg5seywjD2k8a96T42O5Dv7TQf6gkIXyw3l0sF2374I9HXGwE1QGdCpm+7vSzIE6a3jCaCpvJOBhFuXgl59FDqyLLPwReB+WWMSYfPxcibxGuoO87lZEa8uPQ7+w0QoWfJ8z528DznWXMS+6w4wHIsinr+ODvyVPgSDw/9pHgjIjOUX3XWjcZtMWYwfmgxfD3XX4zvprsTHW/xcZapgL7vcFzRxQg4XeW+B5PCHeP0u8Tgmw8iOOC2bvlZivo43Et3TwQNUr8ac6w5PCp0LONvRo/FXQt3QyH+MMin5aaEx/iYxVkPZ1GNksCXqklS0LuKRHWJLmIyE0pZwi+s26j3CzJWz0xmphOfvabN//uT2k/QJwqybA5TJQbL7S6lm6yqHnUsIHMp9aJx8+cCxIEcQxMxQa+ICtAn0yy71jrHXnllsg789bFs+BnKanVRg26aRsHmY+fmQzOfp02wpI0feCbeggGkAjf+aMBfP+ea3tHEMTxk2oFStV3xs7YfGcz0aLe49oe1dOo1zUwcZwoii1F4U9pRbNN0RDvO+u2p5SbYdXTWnnXGKKJiNddiU877HgJx2SB1zmE3H8IUrR9l3BE/SWqczl5cozffvabN6mQnjhVEmFzcJQbL7SKWeI6ogz/YPDiJhQ/+1xwdCh+TiVU+HmyTMUGnmeS2abzPvBgczewglcUbeUGbOZBQxWIQkYyMVyQFaDYT0rDB/omuLttH0C7i6G23922D3gZPfCeXf9k4T63RMtfDCfp0cEF/GQJWa5G8UD79AnQ3fkCpVB8Kj43fi/p+xq6yMn9SBdW8J5t6LthI7+4MfbF1ke7wuxdUa4VvwvrF/JHAcB3nV80GABm6gdaea166icwUw/ZeBDj0qfvnBMvSoqyLuJoD0DiAS+zvMePii0BcP0S8c/DjgelHCsk6nluzSLo6DniwW20fScIgpgg8bCZE+XCN40VWnnx+2KqavRg0NomFD+P8YLENECFnyfMdNjISaUVDfDsumkHu3cAnsfMDRYeRPYLGdLCogJmVhw3MO3wXacSmGZwVwx7XdgU+Y6l9zMtGp/uZ/VfDbMYcV/2uhj2XS49mshKs71wrZUe2f3udrD2zLk9jHL77l2xew/WyUwjfKskF4FDsT8H4HmP2CFmRUl//ijgWoZpt1+EF7aNPNsqYii4jQcAsvEgjg/X4m5Chu1ptVarWR7QZZSP3zkIXY308ML5U5yKbjKl3IzOHPq0OilyIW+Ib9/X6JFzKslJvw/5BoI4GYaMcuOG1tF6IicWP3MuSBDEMTAVGfgBFGUpkC/PkIHXaq3qimabzDaNeFNjUNvD6zdZvC39CF3YvfZL/uc/rWzsd7crn99uLEUfl70Y/oWCxHCkFKde18AYsw3GRxVNU5KierPzyQb7TJW5uI3c0Lfvf/Jxu/dVzzbuyq17KlCQ3wKepYZfB//IHeV9D2/N4llXq7WqKhfQN63r9AdgPMjJgxibwAc+dTbYJyfvWrzoKTw2PeLRpyQXAQwvdvd5Rd/wAP443Ems8WAvLBgCrmlK7ME2c8h/uOvB8xL6JLah75Sb92lPP0VQqTxx+oQ+8FmPMHlR7nHbB14+BTB2aE2UVP72Sw/+lX5lpBOMn/yrX/1Y567EXmSSR1wgqPDzJJmODLzwOru5WtaU6PxPUbTVmwrANh0fwMv+89Vqq67NhmaYl2bmVxO2RfXlcHBGWe2VZnOtCj8KNb/8/ahrl5Z+fmcOgLchcsp5i3n6BABuNML1zMxp9ZhK6CbDFYUfdSpaudl4/xuf/5UIuPyjaO6lueV65g642/YBv90d9vYBwN958Dz7Br/9GgD8h7vPUNCuhT8e5ZpWwLPdh/7AUW5Bd/P6HwXnuJJaWisrYHuUhCeI02B2uZUi5ywtLp3J23YGwZuBot9v36kERUgFWYEnwgbHtfoVBT2ulzY8zGjNVqvVqmlgpriItLA4C4890mp88TXtEfMwKxfyhqRSo1XTAKUcXA9Kudmi3TtBEBNl2Cg3cmjlJZWdmG/c19/2ffNk4yeAz3+qb3cxz4dqRSqkvIBQ4edJMhUZeNHLvXJnScVSKWlz5r97g1vBlz7Q7J6K8kiLs8664auvXrQ31p13g12za9W3w8EXXjTEM9Xff1AxmYich/vP+9akx+0/Yuvz3a2NfcQ68/MXA+Dll5Xt7iuxnH1Wr8jxk9o3FmufBkqm8GOf+fY7QPf1TjT31f52vfK9ZvxeeNY9/u+c208g3fm0dSd9b07FsIsrS0CgSXf9w2r043EtnYmO/fxRAMAf32ikP4AMSMaBnDyIk6MgK2Ch+pLP23YGo94qK8wWunVwt2xPKa/FKqKi7I1rmQkppwjf+k+PX2FudZM7K6nVZvkg8uN83fP+8KX+Q37nYGRTDuIMEC+PJ+N34qyTE+XCZ7HxQiu/shk8NLrWdhe4lPXOicfPx/VP9jGjNe/xB10e48kG/sJAhZ8nz1Rs4MX2LzMM8N07wIWQgv0h98cUhek8WN+l9AAAIABJREFU9Cmh5Bqv0A4io8ju80ol37UMk4WewADgMaaEZh+WYbNcY/Xe0lNOWJaevxgAQLftabVmVZXEoDeEBnOA56Fca5aGnzvo9vNmrtsetBUVCLLoScfmyAAPuaM8HWevO7LwsA/a94/UTEsQxMR5+gT4tr1TsT8LDI0btY5uxsLeWwqe8d9rLpb5dWRWPAP8QLxLKjXq7VJd1Ktfulp3YhVRrfo34RCgrGam//2df3kBYH9D1zfiL//6cVWdfbj7DIp2LQrH1zTlERM+xv2GStJhJ4xoxNRAze3EGac3bJYGRjmhID9yaFWrreUnetAUqVydw+ND9DLx+LmA56+AV8xIPAJ7/yBOaQmCmDRTsYEfESHuFsjZcV3OciSYLqnctGPPrapqMCoMjeKnAIJIFk8qXddslrlB74+iaCvRZ+cvhr80ozXFsKTyGJ57ZpD8tLLYBIcXzp87+Pb7zAskAyfRpc6Par2ULsDYXHS/CnLyII5AvpXl2+8A7PFv/zrmA78V79T0nYphi4T57LyEx97G3kpkVsz+TA4OMiv1x5I4N3QqRt1yYz7w4RB8p2JsVLKKgnprglxLN9mf/+XVwMf4+oeNZMXPQfBFnyG/cwAFm7ouClUSOtEjcsHrgE6WdFj7YAdk/E6cJskw2hs2+0a5WCWnuzdOaHUtfbub8oEPyi1jTDx+pmzgA6P7MdvgKX5OF1T4eSpMRw/8KPCdZcxL7rDjpRU/ueTnQSfoOMormkwkjvtIcEYEnUGtVrOmKYCiLUab9eEWg8tvxlcTZqqHId4yJfqhhpg7Ys2o71oVI8Ovb3ykUoN/uwAAilZrHlHL+iLTr2aJapmIoWFmPEKlOxl7fOCjr7nNRky9ImlWHEQj7gO/EvnAB0omgUPyWnhoulZWPHtrYCtlr49xL17nMG8Ihx0PXnElDOGLu9TEedb52W+yDyX7vU4Qp0SvD3xulBsvtPb6wGtDLW4S8TPAtfhjrUKWHgRxnExFBr4gK0CfTLLvWOsdeeWWyDtzQfOzYD8pqdVGDbppGwcY+hQyP/t1FhCWpErqZFWSi0i7jfLOB1kCkD8K8G9XLPnv7iVGCYI4QS6mD3yqAIm3k26OVwJK1S6nDv0IiDPExfGBF3HUtXRD3x0zB0+/vFMGFX6eBlOxgZcWFhXbY5vO+8CDzd3ACl5RtJUbsJkHDVUgOkRMJoYLsgIU+z2Q+kBftTTeaNTu58CR9W4vowfes+ufLNy/Iw1eDCexGiHgJ0sItJtjwm98F1zk/376BOjufIFSaGMXnxu/l/R9DS0W54bmIZ5t6LthI7+4MfbF1ke7wuxdUa4Vvwv/aBVkBWFvvWvpJlbLBynzUNdaF2KBijJ/2etpmieGhpw8iBMj6sOEVmtVC7xoc1xCh+TUNYo5U5yKYXuJI8Wj+Rgn8eIBlzgLhDJ1+d3vpGZHnE2GjHLjhtaRfeCPJ37y1sghJZWI6eb11V+mROzC109+MReHqdjAh2bl9UQvhecxcwNhJVG/Q0R+tmlW5FrQHi703bRaq6ry0VAOlOvUpfPLR6X7Wf1Xd+7fHrgYcV+hnlug5pYQ8GN7IiUkev2Tn7Rdcf6C30nW3B5GuX33bmj9ydfJTOMgeCs/XGDhGzzvkQfMikPmSKfub7BpMmD2v9jd8Ag6fsDM57YBzC6O34B6kaF9O3FynLYPfCBXmjEheRCa0JfPGSLOOHzTzjfntD8nppG8KBcG0PFD6wj2PccaP3szTgRBTJDp2MDnU5SlQE09QwZeq7WqfPuf1EgLGoiCw4HYaWis72hUZpdb928nXvE//2llY7+7Xfn8dmMp+rjsxfAvFCSGI6U49boGxphtMD6qaJqSFNWbnUfiXDdTZS5uIzf07fuffNzufdWzjbty654KFOS3gGep4dBwRK3WNGYy26zzr7vd2OdIc1cuwXuVmvuHvZ9G5EI2HsTRefoEWW4aOUX1vWbFxQGfEfjAB2Xx3JCy1qqqyWIdAPwZ8yDzYTc8whTJrHCN/Fzy8S90O4hZ8/PglaU5Q3wVyxrbDu59bnbQCShBEEQPT58AePIUyIweOVGu2/aBl0+BtzFGaOXhLb5n/mLHw/MrmWWkxxI/g2u4lm7+yxUqpLw4UOHnyTMdInbC6+zmallTogIeRdFWbyoI5I9e9p+vVlt1bTY0w7w0M78aex5Uq/XlcHBGWe2VZnOtChdzMr/8/ahrl5Z+fmcOgLch5JDyFvP0CQDcaITrmZnT6uFyfGeT4YrC46GilZuN97/x+V+JgMs/iuZemluuZz5zd9s+4Le7w94+APg7D55n3+C3XwOA/3D3GQratfDHo1zTCni2+zD72HhmvhZ9jr///BXeUoI4L36uh/3mEgRxvMwut1L02b0XZAWR+tJIPvChbp3wgY8OVOHZ62JIOCRnnSn6jmkLU6ZWq9WqaWBmoDknld6bRbsd3MbybLuN2fd4rVDfIWlhcRbs12gKDbubV/a7mPsT2r+fHVJ+7wQxlQwT5cYLrVwR1KwEodXa7vZ553HEz1Blj4tRPX9eIB27C8brq7/k/w35/j94/Lf8v2Nd1XllKjLwopd75c6SiqVS0ubMf/cGt4IvfaDZPRXlkZlGnUVx7NWL9sZ65EzkWvUoyL3woiGeqf7+g4rozQYO95/3zcjE7D/i6/PdrY19xDrz8xcD4OWXle2uyEe/2Gf1ihzPPr2xWPu0EVYvxT7z7XeA7uudaO6r/e165XvN+L3wrHv83zm3n6DXdCQ4cV1ZAgJNuusfVpPuIrH6KVmY23PLk79KS/NrKx//gxqbu/HZuLVXF9OAhGw8iFMgZVaslJu1omEOruGUSo0mKkZgVlxOHKi2mnI41Df37z/c4cLHiV5S1rBubVYl3/mqi/n57raubwMA5ufR/srxb5fyhg47XbylREbG1zTlEfvvLpZoD382oS53YkoZHOXGDa1qtVWDboora9ocWKYP/OTjp1RqtGRL1/Xgaldu1mj7TvSBKkaPzlRs4MXmMPMpiu/egaSEcNDZc0tFcHaphJJrvOvc3nJLfHO/ycInSN+1DDNZ2uQxppSbrahDPNdYvbf0lBMeruYvBgDQbXtiryv64xOj+XgeyrVmafi5g24/b+a67UFbUQHRB5UqlgoN8FQJgFrqZzA/eC5BECfEYCeMIh5W9CB50yiJ9hgAgGcbm+Vmqxr+1ipyIZJiGktbpF8JprRwQ7HtuCAodx6WEWgxl3/SuCfFx3Yf+qWF/kPyHoNSXksusV/UIk4cSrkTU0NvGE2FzWQcjKJcPBF09NCqyPIPgddZPvATj58lCeHBxO5iuWjbB3889PeLIIiRmYoN/IiIzp5Azk4Eo0gwXVJLa+VdgzsTBaPCkThlJATEPemk0nXNZpkb9P4oirYSfXb+YvhLM1pTDEsqP4TNPTNIflo5EMcLLpw/d/Dt95kXSAZOyAl+klxQ1wqy8SBOBmbvlputoAxING+2SqEgpWHJMUVOY6/Gg4pr6WY4xB8W+bTUnPgQH6tkGnFGNkuCyJnSB1K6Skmfy8yhQIwppf08ZoC7mHVAx0xGHPtgB3/3p/StJs48ybA5TJQbL7S6lm6yqPbUsIFM4fhJx08EN7Zuo9wsyVtHMCOh+HneoYrRiTAdPfCjwHeWMS+5w47Hy4RiGLbHE7ycPAniRAKoj4dGhFYLekabNU0BFG0x2qwPtxhcfjPdCRUfzUVJKLdLC4tDzR1Rgdl3rYqR4ddHnCb9So+oJIkYHWbGI1TQGRkQa9b0nc1E82a8DxJAXJ9Tva6F0cjdsr1wGm/bFA3xvrNue+GRIqTSWlmJOkH7E3iI3lIhwmwvXucwb0jc+KYcNMGXD8z0rROnxM9+0/cUMmeIIM4MibA5OMqNF1r5rCCxIpUaNW2oxU0ofvLtO/W9E8RJMBUZ+IKsJDSL4/iOtd6RV26JvDPXzuj1kjt5JLXaqEE3beMgM3+UyeDy1dNGWL6lK7ZSp7AA4ge6+RxlLkEQkyc39Rw/04zSOHEzyGL05szTQb9zkGiKUqutlvjnYccL/SX5JywsKrY9oAapn4fo6ERP1cKhY1O4do4Ilb2cJPTdJs468bCZE+XCN40VWnlJ5WIq6XQwaG0Tip9B9n0Cz230G33OoYrRSTAVG3hpYVGxPbbpvA882NxlHo9liqKt3IDNPGi8UzE4REwmhguyAhT7PZD6QN9m627bB9DOdODopdv2AS+jB96z658s3L8jDV4MJ2myyQX8ZAlZtpqHnchY5OkToLvzBUqhjV18bvxe0vc1dK+5+5EurOA929B3w0Z+cWPsi62PdtkjDwAU5Vrxu+wG1t8h5a+S8lRxLd1kM+Q/MjJk40GcPKmK8wIv2hwXv3OQklXiFHOmOBXD9hJHin0qpRS5kDcEpGuYAJFbopPEU2Vg9/uoanakfkecIkNGuXFD62gllROLnxPcvhPnnddXf5mpPE9PrSMxFRv40Ky8nuiN8DxmbiCsJOp3iMjPNs2KXAvaw4W+m1ZrVVU+aq87C41Ip24ssaUcup/Vf3Xn/u2BixH3Za878logRGenBfzYnkgJhS6e8U/arjh/we8ka24Po9y+e1fs3gUeM43Qn5kfLrDwDZ73yANmy+nHYd+532Mnz+eKuz60TAbgBZS/Ts8lCOJs4Vr8zDQMGEe0fpTkIoDh428gV5oxIXkQKhrcc4YkFHsqgYgzwWQ32/w4gPbwxGmRF+XCADp+aB1B/XeC8dN/uOvB8xKnErah7074YZogiIDp2MDnU5SlQE09QwZeq7WqfPtvJo4v477Dtsnip6GZrsPDMbvcun878Yr/+U8rG/vd7crntxtL0cdlL4Z/oSAxHCnFqdc1MMZsg/FRRdOUpKje7DwS57qZKnNxG7mhb9//5OOenTfg2cZduXVPBQryW8Cz1PDr5CVca91k6fcgFFtN3PX8+D+EiwrZchBH5+kTZLlp5BTVx7PWvOqnOOAz+JFdVBbPDSlrraqaLsdBKOWU8RQYHmGKZFa4Rn4u+fgXuh3ErPl58MrSnKHuN5ey0mK/G3AzBEEQSZ4+QarQME5OlOu2feDlU+BtjBFaeXiLFw19sePh+ZXMMtLJxk9JbbRKsaIBzhEepolzRqo4lCpGj850iNgJr7Obq2VNiQp4FEVbvakgkD962X++Wm3VtdlLwZeXZuZXE77D9eVwcEZZ7ZVmc60KF3Myv/z9qGuXln5+Zw6AtyHkkPIW8/QJANxohOuZmdPqMZuPTYYriqj11MrNxvvf+PyvRMDlH0VzL80t1zOfubttH/Db3WFvHwD8nQfPs2/w268BwH+4+wwF7Vr441GuaQU8230YHBu7lm6YDFpN63U0AdRqs6bFK+av/RXp4xHEKTG73ErRZ/dekBVE6kvcWnII1FtlJdStg7tle/EDVXj2uhjieajMp0DfMQNTplar1appYKHmnFR6bxbtdnAby7PtNmbf47VCfYeu1htlJSZEujoH4MrNn1AkOkfEq/HJl444HYaJcuOFVq4IalaC0Gptd/u8c+LxE4C7x2LXI51jgvMHj/82nl6Kf/n66i/5f6e0tOlmKjLwopd75c6SiqWUnbj/7g1uBV/6QLN7KsojM406i+LYqxftjXXn3WDX7Fr1KMi98KIhnqn+/oOKyUToPNx/3rcmPe7fGVuf725t7MdDWf5iALz8srLdfSWWs8/qFTmefXpjsfZpI6xein3m2+8A3dc70dxX+9v1yvea8XvhWff4v3NuP4F059PWnfS9ORXDLq4sAYHq3PUPq9GPx7V0FuvYl4W5vWuBZQj1ScAh/4HB0k12OT0+PBfTgIRsOYhTQOJGl0HCXik3a0XDHFzDKZUaTVQMke5OlHAKJ+EwE94n9+8/3OHCx4mkOWtYtzarku981cX8fHdb17cBAPPzaH/l+LdLeUOQSo2WbOm6HlyuUL53hxJI5wbasRNnhMFRbtzQqlZbNeimuLKmzYEdZrztOOJnstCeIIjjZSo28GJzmLlv5rt3gCsZB7vHoLPnFrccXrc9KKHkGu86t7fcEt/cb7LwCdJ3LcNMljZ5jCmhW6dl2CzXWL239JQTHq7mLwYA0G17Yq8r+uMTo/l4Hsq1Zmn4uYNuP2/muu1BW1EB0QeVUp0LDfBUCYBayjWYdy2TiUMO8m0iiFNjsBNGEQ8repC8aZREAwwAwLONzXKzVQ3DhyIXoqrKsbRF+mlZSgs3FNuOC4Jy02MZgRZz+SeNe1J8bPehX1roP8SXxv+OCJvm2tj1nxfzGPHMk9Y3Jht54ljoDaOpsJkMLFGUiyeCjh5aFVn+IfB6vrfs8Tji52EnfCKMO4uMA8XPcwPllo6PqdjAj4jo7Ank7EQwigTTJbW0Vt41uDNRMCocieOnAIJIFk8qXddslrlB74+iaCvRZ+cvhr80ozXFsKTyQ9jcM4Pkp5UDcbzgwvlzB99+n3mBZOBkKqTE9n0y3VIX1IWCbDmIk4HZu+VmKygDEs2bLf7L6zsVw7DkmCKnsVfjQcW1dDMcEhvkZqskpefEh/hYJdOIM7JZEkTekz6QygQlvSpzhsRNbdketPG378TZg+ziidMkGTaHiXLjhVbX0k0W1Z4aNpApHD/5+Ol3DqBgU9fFrmzSYtAEQSQ4fxt4vrOMNd8cdjwAvdpEQMcXCiN5RT+JBFAfD42IqBCKK7ZBW4w26wMXw993+c10J9TQsqJJEyRuvzd47og1T/zOvIk1OPlOZYLb94sK2XIQkyNVSZSq74w1a/rOJovvdHtc2yN9TvW6BiaiUWKDnPBb951124uigcSPIQfXIAUeordUJO01Y3idQ8j9hyKzOHePDfLvGASdmp0pcornf/abN0mOnjh+EmFzcJQbL7SKWeI6ogz/YPDiJhE/0fHgaSutRnQSq3fyPZP7QvHz/EC5pWNjKjbwBVlJaBbH8R1rvSOv3BJ5Z9eKJ99PE0mtNmrQTds4yMwfZTK4fPW04aahPWerWVms6EA3/4oPdzMONZipT9zNjyCIocjRnE+eaUZpHBEZACQe8DJPB/3OQWKDHCu3POx4UOLukz3PrVn08xAdC9/ZZEgYgxAEQRyJeNjMiXLhm8YKrbykcjGVdDoYtLbJxM9UAWfiYJa4sFBu6fiYig08zySzTed94MHmLvN4LFMUbeUGbOZBQxWIDhGTieGCrADFfg+kPtA3wd1t+wDamQ4cvXTbPuBl9MB7dv2ThftcCyl/MZykySYX8JMlBOZL8VRR/KT06ROgu/MFSqGNXXxu/F7S9zW0baj7kS6s4D3b0HfDRn5xY+yLrY922SMPABTlWvG77AbW3yHHXyXOd0MsiYggWw7i5IkZB2m1VrXAizbHxe8cIMvLrZgzxakYtpc47etTKaXIhbyhgJ6na+Ksk2/qPlC7jjzhiZNkyCg3bmgdraRy4vEzSbK2iSCIyTEVG/jQrLyeUDzwPGZuIKwk6neIyM82zYpcC9rDhb6bVmtVVT5qrzsLjUinbtKp3+5n9V/duX974GLEfdnrjrwWCNHZaQE/tieONEMXz/gnbVecv+B3kjW3h1Fu370rdu8Cj5lG6M/MDxdY+AbPe+QBsz2Pwb5zP2UnL5UarVL8jFmgHEFB6iKTuW+nXT1xLLgWPzMNA4af//5BSHIRwPDxN5ArzZiQPAhN6CPnDAX18ySlPDXw/XnOJpw258SZIi/KhQF0/NA6dEbmWOInQaSh3NIxMR0b+HyKshSoqWfIwGu1VpVv/83E8WXcd9g2Wfw0NNN1eDhml1v3byde8T//aWVjv7td+fx2Yyn6uOzF8C8UJIYjpTj1ugbGmG0wPqpompIU1ZudT9aiZ6rMxW3khr59/5OP272verZxV27dU4GC/BbwLDX8OnkJ11o3Wfo9gKgVCM4whAgLVc9PhFTxEv+SAijRj6dPkOWmkVNUH1fe4FU/xQGfwY/7orJ4bkhZa1VVXsqTMMIQUk4Z8SA8whTJrHCN/Fzy8S90O4hZ8/PglaU5Q/yan3zMQC08BEEcgadPkFNomBPlum0fePkUeBtjhFYe3uJJ7y92PDy/kllGOuH4yaP4ssa2g78dc7NHlRIhzhHhYyft5CfF/3baCxgK4XV2c7WsKVEBj6JoqzcVgG06PoCX/eer1VZdm70UfHlpZn414TtcXw4HZ5TVXmk216rouq7ruvnl70ddu7T08ztzALwNyx24mKdPAOBGI1zPzJxWj9l8bDJcUfijpqKVm433v/H5X4mAyz+K5l6aW65nPnN32z7gt7vD3j4A+DsPnmff4LdfA4D/cPcZCtq18MejXNMKeLb7MDg2di3dMBm0mtbraMKLVvsWYREEcaLMLrdS9Nm9F2QFnr0lrB+5teQQqLfKShC4ueZ7/EAVnr0uhngeKvNM0XfMwJSp1Wq1ahqYqYsgK5Xem0W7HdzG8my7jdn3eK1Q/yG4lm589p24ZK1oG8H1iLNKvDyebN6J6WCYKDdeaJVKKxqYWQlCq7Xd7fPOicdPaWFxFuzXaPKR5s0r+13M/Qnt34mQP3j8t/GUUupLYlSmIgMverlX7iypWErZifvv3uBW8KUPNLunojwy06izKI69etHeWHfeDXbNrlWPgtwLLxrimervP6iYTITOw/3nfU8U4/6dsfX57tbGPmKd+fmLAfDyy8p295VYzj6rV+R4IuiNxdqnjbB6KfaZb78DdF/vRHNf7W/XK99rxu+FZ93j/865/QTSnU9bd9L35lQMu7iyBASKddc/rEY/HtfSWaxjXxbm9q4FlhLq40VYOLQqBvMAKNpRFKTIQTSEHDiJ40XIHAcJe6XcrBUNc3ANp1RqNFExROFPItetVltNORzqm/v3H+4cAkj1krKGdWuzKvnOV13Mz3e3dX0bADA/j/ZXjn+7lDMEZ5OhcPWNw8diHTWNkQbTdEHd7MRUMDjKjRta1WqrBt0UV9a0ObDDjLdNPn5Kh50u3lKYEVRvXdOUR+y/u1iiPTxBHAdTsYEXm8PMKMB370BSAjPo7LnFLYfXbQ9KKLnGu84Dzw6R3edPkL5rGWaytMljTAndOi3DZrnG6r2lp5zwcDV/MQCAbtsTe13RHz+EiVKA56Fca5aGnzvo9vNmrtsetBUVEFvwVA49aYCnlvobzB92PMCzwx2lx2yDjes/QhDE+Ax2wijiYUUPkjeNEt/o8rDn2cZmudmqhuFDkQuRFNNYNen9KnOkhRuKbccFQbnpsYxAi7n8k8Y9KT62+9AvLfQfkjseoP3lxx/XxUhvoevw0DHiCZBp7U7feeL06Q2jqbCZDClRlIsngo4eWhVZ/iHwer637PEY4uceg1JeS66j/1NfPvRbfP6gfNLEmYoN/IiIzp6gi1oEo0gwXVK56+aeW1XVYFQ4EqeMMIC4J51Uuq7ZLHOD3h9F0Vaiz85fDH9pRmuKYUnlh7C5ZwbJTysH4njBhfPnDr79PvMCycBJ7LH9zgEApRwI+/FzBDZu7ou8JSPIgZOYOMzeLTdbQRmQaN5s8V9V36kYhiXHFDmNvRoPKq6lm+EQf1jk01Jz4kN8rJJpxBnZLAki30ofSOkqJX0u+w9RI8/U87PfvPl3f0pP/8QZIxk2h4ly44VWoSEU1J4aNpApHD/p+BmI2aW08ykJQxDHxfnbwPOdZcxL7rCTYTMOAOj4QmEkT0Iz8UTXx0MjIopYXLEN2mK0WR+4GP6+y2+mO6GGlhWNa54E9nuD546oIMrvLPE9PhrpvySSOHgeM/dFhJADJzEWqUqi1INYrFnTdzYZtMgvose1PdLnVK9rYCIauVu2F05L+AX7zrrtKeVmeGi6Vt41hqhBCjxEb6lI2mvG8DqHkPsPLaQT7jxaj2eDRAdkx01Ox/vPfvMmFdITZ4xE2Bwc5cYLrWKWuI4owz8YvLgJxE8ZADP1g3Kz1ZL4JQ3dGnMPT/HzHEL5pEkzFSJ2BVkB2F6mnJDvWBXLcSOptHjy/TSR1GqjpnnMNgI9kSF4+x0AueWrp43vVAyTeUq52Yp/j7kvShL+V2A8e5GBJyUEQRwbWi1HwS5+pimVGq0wp67rup52g8wMAH7nINEUpVZbQTg57HjJc0hpYVHpF/1D+nmIjkRKWYr/LSEIgjg68bA5TJQbK7TyksqRkk7AhOInAETlnIGe3ubwj7/E+aZf3ojySWMzFRl4nknOLKl2t2zmQeOdNsEhYjIxXJAVoNjvHNAHRvLNzLxGZGLuhZkrXjpf05jJQpv1QYvhJE02uYCfLCEwX4onhNInpV4iWxSf24/O7wDvP39eVZeGuFEhLaDV1q7vpc5WC7ICtnP/o93HjzwAULSb7x4Ef7R6Xd5Fhk8pNxslOBXD/k679ocHjzwxt3wDA1dODAU5cBLHSqpissCLNsfF7xykZJU4xZwpTsWwvUQTaJ+HVkUu5A2l9J+UcrNZXjd2qaj+DDJQcJ7U7Igzy5BRbtzQOlrWZILxM10DCqSfSQmCmBxTsYGPzMorndraLZXr1vm+s7Vus7CSqN8hIq88Mity0GId6LtptVZV5aPhFpvr1E3CANjzmGkwrca38EFxVP5ixEx73ZHXAiE6Oy3gx/bEQUbo4hmDmYFmfebcHqQ/eQvsxf/f3v2GuHHmib7/9eUkcGfCzgk7SS+otdsV7AyEhjl4cyIs0xd03gz2MDq+i+IXhlHY2R07ugsr68WYZRU4/WIUDp3DynoxK9sz3CW60C8cwfUK1n3yZoulj9srM9tsoDGzsUn13e6C28nMzeyQmQOZF31fPPVXKpVKJaml6v5+8Iu26t9T6qpf1/PU8zy/T/7vtnlp6Ol366p5pHH+Ubmki+R8O0p/VcTYeeycvn7fEFks9obzoDKc/+aLrfu6s6kYeuu2kEA0psC6OvV2RDRaHngnKNgBY8zXLaorT/T4+6Ccv22IqMdh/7yXe9s/zNfsqPJGTvOqMc+yAAAgAElEQVQ82FptpGa7XNq/2kzveRZlsjnR7eTIP1oRWb7K0+f8Camck0wOsxeaBz4syu3YeeAldmj1vQL66H8YYgbngZfY8TNoUeoXEtgqMehLwCnE+6TJSkYFXiRTaRb3Si1Dr5X6HiytmveGLhLw5Jmrdiqq+l/zNV968w63aro37gRmHR4m93Zx77Y7qafVvKDXNi5f1uS+XqtnO5WMe7jgwqj/aOJb7M4Upx4u9VZJV0u1XE7zTaqn5XL+AfZDZ5n73T8Q+UQOh5++9QV7ArT1Vasn++72bsDeD1Xrq2+Qe7eer+krK7K7a/e2Ovi3oBTzJBAdVc9wd/VfQiRGt/hm58ffjbqy962L6vWzPGQL1ZfIHc6pElJWO5VMwMzv1lROAQ+7O2uF24a8kGtuVFLSredrtbxUO5WMpM6vLrZa+mO71UG9ylpcXbKbUPcPRA5Ui6/VnXXJOZbubPWX+dru4puEoQTpSQvPS3jMo5Ao51tv5NDqhjd7u1/8euDKMePnoEVLq5oYnmYJq8Pm/x75DwlOHt4nTVUixsCLiEocXC3mNLcDj6blqs1IM2RkKp1mMedsqWm5qi/vcLPqLNRy1YlMzZZKFSqNak7E+Oh33ipqInqt3hUxu/UNfwODVRhpl/P58vZnIvKZ4fQ213LFarOSMdvlfL7cNk2RJc+i5tVf/7MhotfKdXucUfpKo5rzdPnUa3l3ocivPizn8/WPfvVLVROvW3MHrKzYp7+kvWyVtIf66zGQSiN3+W3Pl5x7+/Lgsau7u57BUplKp+P9BWlL514WOfoNY6eA+baU1sRo3bPucpVaMoLMlaLmDo/s3msZ3gZVz0B09Yo/sE3RrP+3nS/llbc3VGalTKVZ9ASco/6Dqo/UyMxa3uqd+tnfuvt/UK7psvim1QTb3tiVF+Twp4zhTArevSMZokS5eKHVCm9lZyKPDw4HrBkzfg5elCrcLGpOidUUIi9evhllcCZOnoWd695XSj3/xUQk5Q28iIikMoXKoHTifTkx+pZXGgMzkacylUZA+rT+fYYd5Xf7l2WuFDW9tfXJTbWkfyi4mzbTFBEx9F0R+cJdvJwtZOysHrJ/b72lH9gbrmZlvfZYvbo29FZJqp1OR0TM9t6BeBl6qyTpTqXRKYjZLt+XvQ8/Uptp6SX1B+Ol/9zovGuXL7gFI1VodArddl3OVzIp/4sq1+980/s9mu1y0PQCqhuBZ64T+wD2L8js1ku1Ay1K7/tApzODKDk2cQw+fiLy690Py637dkLjRnU/X/P0e3pZk0/VXX+4a4rIL9yRnC+IvGStlSo01nYLa1Z3nufPrbU9DaqdtX9zFolobwe20pof/vMXIvLJ7Xz+tvfjzZ1KZvHR1qei5d5w+2O9kdMe26+8ltKaiB2Hd3edOGb+RkSeP/wgn/9ARERy1ebqhkpxPPM5URELL+ExD/rDZmFolLNmkB85tGYqnTef5O2ektq5V2TH/0SoxIyf58NCa6rQ6KTr+XxeLXn+3Nr73yNyAtOSqAp88ninnVNNqFquauWVU4PfAxIk5aqdSkZVpr1J3A1dz1WbjUxKpUlv1QzNTaPsZIuzJq6r2qnk1VJfMnjDECe3qDgvyJ2pTCqDp/Mb1Hpin6gzeN8dgt8344nqij+om77VwqEV5yCPAHAKvfqayOHATBivviai73z0R5488Pe8IzXNdrnUsnIfLa6kZMe4vX3VTVas/8e0E5bWdlJqM7NdLq3Vu5488M4iMdvl0u1y+5v94SD1vfc73/N90q3na/qFi+fsPMbZdxoV70J9z9qy4GlQXe4ZZHTB1yzZ3fZkPx7J6WxGnKG/+KfgRET8IjAD/jDaHzYHRrnFlZTsLL8qItLdjhNau/X8B4c9eeBXFvsKGD9+hi6STKXTqag3PD9YOxfz2+O2TTjeJx0PKvDHReX3KLpZ4VMZlfzTW7deWZFdNZbcGivv3UOuqrZN9bzDVpP0WytZD6emaXa7j/a397d0va/jVUDtuVvvnYh0RKqzgdEztr9f917L8A/59zjYNzRNMwzDaNXKErcwpzSlJDk2MTEnNw98yITI8bbCHBhUe1eL/usfUhnAzJ30PPBukOxu60xCDEwdFfjjcrBviEj/HJ0ibidz7aWvuLXt3nwdWk86IzdXSCq97HlPFNBR3693RyJ6rSYiYozTWTRVaDTT9fWabmWCq968sr/eV5tXkX3QJIGZSkf1Ze3WSzVP8j0Md3TuTuAQI+YLwegGzDmv9OaBVwOHvHFn2V05ch5468eDfUN8w2f6nluDTC6P8aTQajY/+F1g9vrywAdHOWelWKFVvSda7ckDvzesbBONn2Z7Qx/4kiYq7tlk433SsaACP1VqbrfoiYTTf9zpvDPWEVUkFi2XW01n0+mlpczBvXxftrleWq56M71RCurQP4KeqQS623253CO2zKasyQMYfArMuYTlgQ8RbyvMgndwOznhkSwnNw+8SEDrBE4d3icdDyrwE3C4a4oYPfnrNC139VvSckLZUloTWQ57sTUJ6tWW9yjmo72wDT5+IiKLr1cyKVkqbpVadrq7IT6TgPyeZrd9b6Nlv4LPvaTbcd3fKcDTPVcrqgn4Sy3jjTeLX/2pvbH2xvJvRZ6LcsZwkGMTx23WeeCtVEVBG/jmzzT396I92MbbCsdJ1didajmVcyRLWJRzAmj80No/c/BA8eJnaJBUb2kImsDUJSeNXNIYhl677ekvnjq/qoleK7e7dhw2u+1yPp8PyNk2Jn3bPobZravkI3v7w6N/qnA1J8FJ5PzM9o/7kr6b7e/nS7WWM+De0PXHIourEdphU+dXNZHHH3g2Nh7rB9E2Rq+jc3fUv0ErqHwepPRAoI+fiIhey/cIiQr9yYqHsfPAO1u1y/YRltKaGFuPPOGqW8/ny4G53NS4TVEvs7xlTJ1f1cTY+ZFb/B/tGP2v0n/1S9UM6dtq/8A97H/ZCtgKAMJ9/ER8wcUvJMod7pruhiOHVn8MExH5+w8NMXcDc8nFiZ/DQqv5f/61LuoPSHDQRrLEflzseQoNfyhFPFTgp82pOqv6sdGqlazAV6qFzOcWk6oM6/YxSrX+KexCZCrVnLjpmYOY3Xq51Pq07/ODnwX9iThUf0lShUan0+lUcyKSqzo/2tPwpdJfDToUL+AnjcyciGbxzU6PAd1yZpkHvl2zUlY0rZCi1+xn0FTh9UXZ3bVP483F3V1ZfN2/k+7f3P/cv0crh3K9a2cx/tQYPGEHZsPbYZ7E70ikucgDHy9+hoXWbj1fuv9ba5fV5VZpCq+ncFwm8rg49H0SxkEX+inRtNzq1W/s127r7kjuTKWzJt9/Vz/8UkREnn9h5XvvvXtJxWyV2HP3o66sb7QMQ0SWXnnR3pm18NBM31uv6Ya8bCUF6dbLNd0QeXlR5Cvqo1ThZvVna7XHbtReWtEOdq1JQg93TZHn9w+tLu25arNnPomlr70oYrTW2ud/HPDcavW3ylVzT2q67++Cuf+ZyFLujef0x1Yn+NzVt9Iba97Jp9wJAazOYXYm+O72rsjSuTee23E3vhmSzQ7AfOhJVqwVm9XlUm14H85UodGUcslOVuztwpmpdJppZ9HACfXMRx+q90y+saR6o35lo5Iy2z89lJUVN6f7yors/rRtftcOOfXgiUEylU5V8s4YnxdyzekOeQJwGg2PcnFDqxXDrD3ncq+IHpgHPmb8HLxI2hu6LJ379wc7VjmqOd1OLgJg8qjAT4DK3Bn0mGkWP/TMxWa2y2ueeu+XX+zeXrczHKvEnju3azv24oNPPrfne1tcScmOfO0f7DnePz0UrZjdL9vv1z89dAcdmY82Hvvq1ge7hpNWRO1o733Vlqull1JpT7pSs10u3f/ck0O+T9pa2K2L3p8r+rn0Hzc8c/CZ7Q3faCl7VtVuveStvltjpt5am9SjMhlEA5GZE1GE54EXEVmWR+W8/fKmUVAPauop02iVNorNTsWJH1p6ya0sx8pTOagLe+r8t7RWyxt2VdLjtDg5O/+s8W7Ku8yKxGa7XNO1YvOmrJday71nmqk0i3ulrdXicqu1lx6xsC6i0DT0p4v70w+FLHGYO/1htCds+uOgG+U8eeBl/NCqpdN/IHIUlAc+Xvw8P3hRet8QyV38679es5YspTXRY05FTPycLR4XE4EK/FSpsZ7q7bfqA6XlqtbLZbPbXq/1zftuV5/VvG+65wW2oeu5arORSZndeqmmt2qGVmx21PNoqWVYNWU1UMpTCTfb5VLLux8xDFEbioh4h6GquUhDXn1nCpUBS9SJttbb6ZsF5+xahkjflKVmeyMgk5yWXvJMgccreGBu6a2tYrPTsNvf6vnanhNQzHa5VKqnnadCo1XarnY6FWtFZ5F6WHQDmGcb7yK1rCxBT6dumiXLwb6d9sIU6Zl8zptq097QbAedndleb0mxWUjfG2MyfQDw8ofNKFEuXmjt1vM13Xqhr47S/xQmMkb8DFvEjCHA8aECf1ysRk23XprKFG4Wt0q+DMe5asV5XX011/L38rQXpjLZnOi68w47dX5Vc4ZHWVHZNM1u99H+9v6W3j8KPmBYZ7fem0lkRCrxm9GqDUl00r3XO/Df3N8Tka31kuEU1DD0WmkvbmnIMxmMzJyISvfn1Ojp3+kJIGZ7Q5dc1fl/X9Z2917PZHOiW+2M3Xstw9lMxTqrr6XZXm8ZWrFZsOPgzeJWKUJ6SzUhk3W0g30jKPOcNZAofDeq+p6SMQdvck9N3KAR73/xT19nInrMPV/YHB7l4oVWaytrP1Y3/L3hhYsaP9ODF53vfeF+sG+IyPCgG4T4OWM8LiYBFfjjooJZf+pPEbeTua/5sjfhZm/bptsK6m8d9edsC9DfSKrXaiIixjiJ11OFRjNdtzv5a7nqzSv76321edVdvr8BwTA8vQbMbn29phvjJaVHDzJzIrIB484VbwBxX+N4486yu3JgEjaV7TLrHCFT6XSsH/uSCPc9twbp1kstQ3LV2A2QVrmc6jsATFBoqnQ3yjkrxQqt6j3Ras9z5N6wsk0kfqqG2NZ6+3yjkLLnAUUy8biYCFTgp8qdt+24qEgsWi63ms6m00tLmYN7wfM1eWm56s30RqmvQ/9oUplKo+N2su9uG735QFX9Pes7gpUT1dNlPpWp3CzulVojpDMFMBOe+eBy1U5lSXXajMvc3+uZVklZDtnEHvzjPn32Nn5ahkRiqu9zLHzCeScnfM/PwByKGOXihtbARtPBhRkpfoaGVt/8eVqx2Syul7boVA9MCRX4CTjcNUUMf6dT0bTc1W9Jy2loXUprIoEz3U2SerXlPYr5aC9sg4+fiMji65VMSpaKW6VWrZ6NUsTPROUq9a3pGcUuWi73km7HdX+nAE/3XK3YbBSe+7fng/6YiTy/eyg8TU+Qaj11GlZpTMW4rIQS7sPfmKl/rea8yKNnrMQYQRv42v/M/b1hD7bmoy1DDMMXiFql/NYYA4swMRHr5KqeTx0e8ywsyjkBNH5oHeHNR7z4GbIoU+m4L3G6dUOWrxI8k4nHxflHHvhpMQy9dtvTXzx1flUTvVZud+04bHbb5Xx+Cpky9W37GGa3rrKHOtnoQ6hE9SoNciiz/ePd/s++ny/VWs6Ae0PXH4ssrp4fHr0Xfz8wDbzIV3+/f+5UjG1oZk6V8JMU8afTx09ERK/le4REBc1zl6tJNIdRc15ue2fQtI+wlNbE2HrkCVfdej5vpzX2U+M2Rb3M8pYxdX5VE2PnR27xf7Rj9L+A/9UvVTOktVGh0elUcz3HIA88gFF9/ES8waVHSJQ73DXdDUcOrSry7Xvyxv39h4aYu4HJ4OPET+8BzHY5X+8e7PtCa7fubvUTvbe/JRLCeQgkkfs8owI/bU7VWdWPjVatZEW3Uq13Prfxpc6vaiK6fYxSrX8KuxCZSjUnom8EPi0rZrdeLrU+7fv84GdBfyIOVaBPFRqdjvVwnKs6P1rT8KW+936z2N8v64Xcu9/j0fl49dTbqcafVotvdnoM6JazlNbEaN2znvxUpo0IMleKmhtouvdahh0IU4WrOTFa63YM6tZrenAt2mzX7KRMVkjRa/YzaKrw+qLs7tqn8ebi7q4svt4zx/Pf3P+8d5/dbd2zP9GK5IFPEG83+/Au98AsRYly8UJrqnA1J3rNbvHs1j8IrLtL7PhpHaDeVaM1P/tbt+Tdej6vZsDvdDrN4qIcivx69C8HM8VDYILQhX5KNC23evUb+7Xbnmk5M5XOmnz/Xf3wSxERef6Fle+99+4lFbMPd00R2f2oK+sbLcMQkaVXXrR3Zi08NNP31mu6IS9bb6a7dZUK/uVFka+oj1KFm9WfrdU8qeCXVrSDXWv+5cNdU+T5/UOrS3uu2uyZT3Lpay+KGK219vkfBzwyW/2tctXck5ru+7tg7n8mspR74zn9sWGd/tW30htr3smn3AkBrM5hN90JVtUMeBu6MxP9UvG9CtV3YL5Z0xzbo2K0YrO6XKoN78OZKjSaUi5Z/dV9XTgzlU4z7SwaOKGe+ehD9Z7JN/xGb9SvbFRSZvunh7KycvhBPv+BiIisrMjuT9vmd+3smfXAiUEidLQHgHENj3JxQ6tvILrkcq+IfhCwWvz4mal03nySVxMfy+6uW/Kl9JLIgTNGMletvlarbbTNDJ2YgGmgAj8Biysp2Qkc3m4WP9RbTg3ebJfXPPXeL7/Yvb3e/qZ6cl1cScmOsXO7tmMvPvjkc7v/0eJKSnbka/9gz/H+6aFoxex+2X6//umhO1uc+Wjjsa9ufbBrOGlF1I723ldtuVp6KZV+TeTwtVfVpu1y6f7nnhzyfdLWwm5ddHsr13PpP2503nFPvr3hGy1lz6rarZf81Xe1NFNpZCrWo7VWrMaP+T//+c/jbnqqvfSv1cDPF3auf/b7tWMuDGblVU9ECLYsj8p5++VNo5CpVHO6VR02WqWNYrPjtr1p6SW3shwrT+WgmedS57+ltVresKuSHqfFydn5Z413U95lViQ22+WarhWbN2W91Fr2nunBviG5q2p/npnx4yAKHbO/+Kfe5EZ/+qH81z/kt4BZ6A+jPWHTHwfdKKeeJtWG44dWLZ3+A5Gjlf7BiGPFzw8OnfhZdbd/tHXgL4jZ7kksFx3xcyZ4CEwWKvBTpcZ6qrffqg+Ulqta862b3fZ6rW/ed7v6rOZ90z0vsA1dz1WbjUzK7NZLNb1VM7Ris6PiaallWDVlNVDKUwk32+VSy7sfMQxRG4qIeIehqrlIB9beRTKFyoAl6kRb6+30zYJzdi1DpK9rvNneCM4kF2EpgJnTW1vFZqdh3aLder625wQUs10ulepp56nQaJW2q2pao249X3MWqYdFN4B5tvEuUsvKEvR06qZZshzs22kvTJGeN+neVJv2hma7Z4/m/p5ospHPW08qsRocAKCPP2xGiXLxQmu3rrqxVzL2UfqfwkSmET9DtwIwaVTgj4vVqOlmS0tlCjeLWyVfhuNcteK8rr6aa/l7edoLU5lsTnTdeYedOr+qOcOjrOBqmma3+2h/e39L7x8FH1A/7tZ7M4mMKHOlqOkto1Ubkuikey9s4H/40oi+/vXeVzGI5F8HLuErPWV0f06Nnv6dngBitjd0ybn9Zfqytrt3cyabE91qZ+zeaxnOZirWbbSvZApWO6fmJHNLqSA5PL2lmpDJOtrBvhGUec4aSDTQwb4hRu5qp+G2JOT3YyYO4ZY5ToNGvP/FP32d6egxH3xhc3iUixdara2s/Vjd8PeGF24C8TNm1A1G/JwNHgIThQr8cTnYN0QkKFua28nc11u0N+Fmb1dSt6nT387pz9kWoL9Pqq7GMxnxOjtZhVDD2Gt2FrnqzSv76321eZUIftAL9vClmK6jc3cCZythAtLTZ8C4c8UbQNzXON64s+yuHDiiXGW7dOcn9nRXP9h3Mm9aR+h9bg2iJlTKVcd7Ye5LgdTTsAAA8XnDZkiUc1aKFVrVe6LVnufIvWFlm0z8ROLxEJgsVOCnyp237bioSCxaLreazqbTS0uZg3uB8zX5aLnqzfRGqa9D/2hSmUrD8wDc3Tbcofn2R7oMTiwSvhTA/PHMB5erdipLqtNmXOb+Xs+0SspyyCb24B/36bO38dMSKxLHe4GE4xM+4Tw54TFvIka5uKF1tGk4JxY/Jxl1AQxHBX4CDndNEcPf6VQ0LXf1W9JyGlqX0ppI4Ex3k6RebXmPYj7aC9vg4ycisvh6JZOSpeJWqVWrZ6MU8TNRuUp9a5rd9r2Nlv0KPveSbgdvf6cAT/dcz18Ns/2XDd1a6k4UgGOl2lmdJliaXU+Unr45ExnhbSWUcPc0OANlJKn0sohEL5mVGOPyZelrfHy6+X/ka2qiZa24trrnebD1fBOfbQfO6my2y6X9q8ET+mC+UD/H3HHywAc9TIVFuZ1dU+Q3H4tI7NDqm6n+o/9hiPnioQQH1Afl/G1DRDUn+IcL7W3/MF97bP3njZzW3zDwq1/2n6Jz7G49X3t6bknkLFk9EoWHwAQhD/y0GIZeu+3pEZ46v6qJXiu3u3YcNrvtcj5vZ96cIH3bPobZravsoU42+hAqUb1eG1ogs/3j3f7Pvp8v1VrOgHtD1x+LLK6ejxS8zXa51Nr9wv6voddK5ZBk9JgqJ2ST//PEMNvlvJo0ydIsSquUn8hdpnnucjWJ5jBqzstt7wyadiBcSmtibD3yFKtbzw8opxq3matWl+/f9x01dX75BTnY+dI63epya61l2K+CunXrm2gWNZGv6iU7BLulsPox/aKnpz9ONJXxmKCHqYsa5UYOranzq5rqNGT7xeBM7DtrhduGvJDrywOfOr+6KIb+WGV073Squce6IYv+V+ndv7n/+cBjd+s1XeTLzw94AZ9MR+fuODV5QuLcogI/bU7VWdWPjVatlFdKtQnM2NYjdX5VE9HtY5Rq/VPYhchUqjkRfSPkqd7s1sul1qd9nx/87DBg7UMVzVOFhvUnQkT9TajmRNxUct17LUNk0VrarBY16fnzhuPSE68J3ydBwByVqUKjWdSM1r3x2g+X0pq4O1GZNiLIXClqbqDp3msZdiBMFa7mxGit2zGoW6/pwfNiqImgcm/otb4hQuajvS9E5FPrQTaTXRGRF5bPp6ypoTxJLF97M2cXRLWx/u1f5q2Oq7/+ly1jwvEZc4mgh2MVJcrFC62pwtWc6DW7LaBb/yDowUxExKz/t50v5ZW3N1RmukylWdTcVtWj/g08H3XrefftfO+x611VfRf51GBKo4QiJCYCXeinRNNyq1e/sV+77UmDmal01uT77+qHX4qIyPMvrHzvvXcvqfB2uGuKyO5HXVnfaBmGiCy98qK9M2vhoZm+t17TDXnZyurZratU8C8vinxFfZQq3Kz+bK3mSQW/tKId7FoDOQ93TZHn9w+tHqS5arNnYsmlr70oYrTW2ud/HBB3rf6quWruSU33/V0w9z8TWcq98Zz+2LBO/+pb6Y017+RT7oQAVr9bXyb4pXPPHexo6SWRVEbNwM/YU2ASutt9t5uIqrDuy5Jp32aejuX6jz1Tt3Xr+ZoUi3sta+Hibi3fOlutilNz9k1a/4KvD+eTWj7vWfKS+sl8tGUsnVtyR4EuWTM6qRztly97pvvMvZnTa3npHX1kvZDSncdIVQit2GwUVEKkN3NuubSXxdh7ZErBzZVk+d20k6s4VbhZ/O9u8+Tjz5wJ6QEgro+fiPx698Ny676TB77SWfu3wpoT5bS3e4cuWjPIe0Lry5p8qkKreiL8hTtI3g2tkql03nySt+Ondu4V2TmQfuaH//yFiHxyO5+/7f14c6eSWXy09alouTfc+PlGTnusu3nga7pWbL61W17b8e80U+lUJV+zE4a/kGtOd8gocKpRgZ+AxZWU7AQObzeLH+otpwZvtstrnnrvl1/s3l5vf1O9FVtcScmOsXO75kTEg08+t7tvLq6kZEe+9g/2HO+fHopWzO6X7ffrnx66z6Tmo43Hvrr1wa7hpBVRO9p7Xz2Ja+mlVPo1kcPXXlWbtsul+597csj3SVsLu3XR7a1cz6X/uNF5xz359obvUd6eVbVbL/XUJ5bSmug7Z+0Dq2Twsbte/fznP4+3IV761+BRvws71z/7/VrgIsw9NTlkwEjEVKHhzDk5JC+xN4mx2S6XdvSalYTYeoi03u+r3fzR+ZTY2TF8eYxbnmGUBzsH9mwdbh5jNRvzR7/T7HTsFbv1vB7wItybxljt2o3AByKipf9TpfPdiruCZ/S/Ci1qD2a77N3pT6wUx+VSa/k/j/HwSRRKCoIeJu9Vz4OV+p++89Ef+fLAr+2krE5RZrtcul1Wz4LqafJVEbvh1Y7CZrtcallp5dTj4u3tq24eeP0/WqG1W89/cNiTB35lsa+Aqe+93/me75NuPV/TL1w8Z+eBz77TqHgX6nvWlnYeeHFK6rGU1kRbbTbS9/K1vXTs74/4OUOExKSgC/1UpdLLYo8KUn2gtFzVGobarBa9XaQs9uJmURPfMFExdH252ux0Os2q6opvj2hVq1pd9dV7Kecg1lLvfsQwrA19U6g4c5GGzB6XKQxaqE60td52R98P6PKlauf+jlWpws1qTnM7/rcMjYwmwERESYVh5SW2G9VShZu93et7u0I6VepMNic9U32oiKcyErubqR7qnkjkz2NsRTDVudQzfqa7rfuGgkbRN8T0YN+wAnHIIgCYlv488CHxNm787M8Dn4tUuP488P0iREmzvd6S/s5eAKaAN/DHRSXoLLqzq6cyhZvFrZIvw3GuWnFeV1/NtfxjO+2FqUw2J7ruhP/U+VXNqStbraOmaXa7j/a397f0/lHwAcOSAgbJjiZzpajpLaNVG5LopHsvaOC/ebC95yum4ZtIdTRf//rXh6+EQP86cAnf6onhyU4knj7nodnX+xoA/DMSB7UPxEsRr3TIfY0AACAASURBVJ5GdWdO+e62LtZrp+hU9Gytt883Cil7RqWhiyaK+yUxCHo4BqcgD7yqvjcnUX3n1pslQmJCUIE/LqpRsz/1p4hbVfU9Bvdm1ex9RnbDt+qqan/ekyyqX//Dtq4GLRnucP3RpQqNZrpud/LXctWbV/bX+2rzqjdvTwOC2V6v6YZWrN4sqC703fp6raW61MYrDOI5OncncKoSUokkmJry3TOjRKaiOl7aHSxjZV+PImYeY9UYuN2tZDLW+6TRw4A1GFOdkVZsNovrpS0V+EIW4RQi6OGYncw88BOsvmOmCIlJQQV+Agbmgb95JULv1cmyEiBpudxqOptOLy1lDu7lQ14yffxERGQxV11Lb5RafbmUBwnMAy+SXtbEMETE2Nt+9JHY00X1tCl4ppYqNhsFebRlyFLum/sbpXxNRETLFb+Vk9u6t28CgHiW0lpPbuBeo2ZfjyR+injnddTS/pahFW/GCwJOO4UqjCHLV1PDFwHAVIXFW3fs0GTywIez5iUOKopvL+b+3rCGAfPRliGG4WuVaJXyWxP+swLARgV+WgxDr5V0Ebun1FJaEwmc6W6SzP091VrrHMV8tDd0q8X/rZJJyVJxq9Sq1bNDixiUB16kWy95GgoMvXVbRmi8ONDvO+OrDL11u2cuaxwXJ/nnrAuCSbF6jd97IHu3Pe1oWnFt1f55yZ2K3fqkW8/X9sZ+9urPY7wcqcSqBt9O7xna6s0IJfjVL/sbFD3NhlruDcP7Ij/aIiNKMMQJ0BP0eNGEcX38RALfcSgh8fZw1xT5zccir0qM+KkipzeBz99/aMjnLx5KYCLOsnpkszoDOA+Oai87P8q37Ae9lRXx98wX6Y26qUKjU/APz5LA8ZpIAEJiIjCJ3aQsvtnxWls7t/i8iJ192J6CpFZ2pnkTs9su5/P5+niJmAPo2+5UcnU1lZyTjT6ESlSv10ILNCgP/IMbNV3khZW31bx61ctq2tOFr6TEyQPfLGoiWrHZkwc+9T9/IyLy/Ctvrlnf3Zva8/KF881h5sgCmmyZytq55/XbLfPcmjO5Ze6z1lrLkKXV86lRsq9HFzNFvIjYGYVbLSPS9HXdv7n/ec8nf5nPl1q/tmb5zH2mP5ZXvpEZtsjtTdosaiKLotemEJ5x3FT4GhrBjs7dUf+Op1Q4veYiD3y71rKS2nXUI5kb7lKF1xdld9d+qH1zcXdXFl/3/znoj7rOvPnW/kQrkkYu0cJDYsS4iunhDfykHH6Qz38Q8PkXe49MKaScael6pnmz53ObUB54NaGd9e4/oJDheeAzlbc1/bZeu/GNzq1LAZuH5IH/zZciIl/s3i65WUVffFE++ZeuXLJDuJOC2Z8H3vxfvyIi8uUnH6z5vsEvhETwwAR062s7X2rFt5dba25WdpXb1+4Wmal0mulyyc2+PnZnoZ48xlqxWV0u1aJ2q1EzdQ5vQ+h75SMiYu7/WuR5+dQeZZp7M6d/8GHbvFRIhSxSM2zmqo1CymyLyGtvF79yu8VAngTrebhU/6WKjpkbHm/jxk/fHB+Sy70iemAe+Ecfqo99Y/H1Rv3KRiVltn96KCsr7kPtyors/rRtfrfgpKoLGpcZoaM9TgLi6pygAj8lmpZbvXrl/P56yc0Dv5TWRPytqEu+F0xf9uaBl8886/7ywXrtsZ0HXuSzbV8e+Bf2RUQkdf6bL7eMvjfkhqdT1Zf//JPW5yIqD/z/9Kxktsu3DXlB5BPrgbaPnQf+//p+36LnRJZybzynqxJqWu7qW+mNtZbn742dzqo/D7yILOUun927r9vdWi9/c+/+/Zhd6MkgOiaygJ4w6r3IzcKlVOFSxfO52S6LLJlBzWR9U2bKo3LeemFTVT3v3Ye4XLVTOKjnS86Tpv0omqlUc7q1ktEqbRSbnYpzLC295O5C82REbkmx2SiIiOSydpZ4PbBBwWyXa7pWbN6U9VLLk5FYNRV6tjDbex+oLqshi2R/zz6kJ0U8A3kAjMifB17EH0L7hia58daTBz5G/OylpdN/IHIUlAf+/Le0VmvZFwnLpZakxUmZ9GeNd1PeZdaT7KCoq+Ju7qqVXrTS6UT4ngDERgV+AlTIDX5jtexMAO3kgbcyyZnd9nrNmTZucSUlO+qleCXjzvv2knsA45PHy9VmI5My1WjzXV2KzY6Kp6WW8VJaRETMRx996u5F7B2px1JrR58/pzYUEenaf2cC5iLtkylYj/99f53UTNf6Z8Vqs+GenSHimc3UeShu+UdGWdt+9J+qzY67re7bFkBMKvVDwHuRVKHh1OftmnPHrkaXyuKJBXprq9jsNFSXmfaWiF7brnY6FVW9ruV1rdjsdKxg5OZoy9f2nFhjtsslb2oJo1WydiHder6mFqnRl1uPdmRL14pNO5FcX+JJ5wQ6BREx2/3LQqbfGLDIzdtkcboMxUAz4szREIk54g2hQ+OtiMSKn+Jv77TyjAQ+SYWEO1Ok5026N9PRwKhr7u+JJhv5vHVzjZOXmPg5t4ir84MK/HEhD7w6UEAe+KjbRkSmynGRBfQkMSOkwjDb6y1Dc1IApVRo8uak6A0azj3s7+vumUTJSgHnbNabWt4TBjLZnOjqVXcq/VWR3daakauqB1dVfx9tOoy+OaJUEs/9g9BF/kOoGZ4GtBwAwEg8IXR4vBWJFz+trSrOxHLV/Xxtb3jhfOHuYD9wujyjP0r6HOwbYuSudhoZe5el/P60520GTi8q8MeFPPAiA/LAR9wWx4QsoCebfwCjVmw2Cgf7hr+W3Pu02Bc0/CMdg9oH3Dc83pC0PGgXtsy71Vy+plt9hqS77XuKjcaad9/tCeCeb8giL5WOM1eN3aJJU9fs0RCJ+eGNkSHx1lkpVvxU74lWe54j94aVbexwJyI92TntULvRvpKJsVfu0PlFXJ0bVOCnKsrLr8kaNQ+8ouWqN0fKAx8olak0PAG8u93XAVXV37MBRxi+LYAY1BAVz8sT5znL6mBpxSmjv3Vxebwje1oKctVOZck+3BCqR852t5LJWO+TRg9JvpmctGKzWVwvbalAHLJIiTKaCPOPhkjMp4jxNmb8HHEauYBw1/vyyBLrSXbYa3skDXF1flCBnyarv/jc54HPXR0pD3w03W29N+QPrr8P3xbHiSygJ8hSWhsyF1sqvSwiE66xWrkm3J0Oz2RpF8d+HbW0v2VoxZvx4pHvfVC3bsjy1dTwRVamDWrvAKYjLN46UTJ+/Bxh3s2QcOfbC/PLA/OHCvyUmGb73nrL019cPZTWyunqzYIKi2oWO2P8fE299O3ulYw1HZwnD/ywkG4Nu49XhbfmyrOn4LOnofNNsj+oP0KUbUfw+uuvx9oOvf7Q/uF14StNLqvXeFjvmr6R4fYESmNWY703sfloK3BkZVCJVQ2+nd4ztNWbE3hqVB3xA8/eu0gNBJ1IRCYKzYM/DPqQaIYZC4m3vvVGjp+eOUisTw72DZHVwJUHhrvgvfh65gfvruSf1t5N6zEy4uc8I67Oif9l1gU4MfRa3qtUaumGuLOJqMdoEaNVK9mr1PrncxtX6vyqJqLbxyjV+qewC5GpVHMi+kY7clvvgAOXai1D+lLFqalR+ltxo2wLIK5MpVnU9Fq+7Lmzu/V8qWVYj4ipwtWcGK11e3m3XtODJrscxVJaE6N1r6v+p5JwRJUqXM2J3moZMZvxuvV8Pl93Dl2u6U6gHbzInlmKSZcATFGUeBsvfqrIWbMj/aA5PiQ83Fl7scNktD8HqfOrmujbXecAj7YMJgEFpoc38FOi8sAXfG+9M5VOM11fb9nZzrXcVXdS+glJFW5W9+3p4ETLFa9eSW+XanrEgUhq+KkzxdNIB/ZNRBdwboMnBBi+bTQ//elPAz93WnMHrTBtMy8AZZirMhy/VKHRKXTree+wS9+rl0yl00yXS87y8V9DWzMg1/JOpuLqcqkWtXunf3L7UWUqzeJeyT6072QGLlJvuPrmGR25N334dZWsy4/STg+lPc2Gx9u48dM3x4fkikWttRWw2pBwZ+0lKIIOlCo0Oul6Pp8XZ6PG6H9DiJ+zQmmnZ0qlXTg6Oprg7oA5NPNbfeYFoAxzVQYM163na3LychAl6/KjtNNDaYFRJes6pLTTQ2mFLvQAgDljtjd0ul8CAAD0ows9AGBO2LmTctUOs2AAAAD0oQIPAJgTvhRvAAAA6EEXegAAAAAAEoAKPAAAAAAACcAs9AAAAAAAJABv4AEAAAAASAAq8AAAAAAAJAAVeAAAAAAAEoAKPAAAAAAACUAFHgAAAACABKACDwAAAABAAlCBBwAAAAAgAf7drAsARGJ26+sbumGIiIim5a7erGRS09h80JpjFcDstu9tbNlbq82vZFJ925vtcqllBO1BKzYbBYlfhiF77tlLyMnG+h4GHz24GJFL263na3rQGYSWZuyLAafZzGPR8ZV2QoEreqHHKO3EQlx08xUMoyNsYnaIn8TPMXZC/HTxBh4J0K3nSzUnCooYhl4r5evdiW8+aM1xCmC2y/lSreXZWm1eKrfN3nUP9gcHpo/G+hLC9twj5GTH/EVEFrG03XpNH3XX418Mox4RJ8nMY9GxlXZSget4SjupEBfdnAXD6AibmBniJ/FzgjuJ4ETHzyNgvh188Off+c53vvOdP/+rfzxQH/yj9cl3/vyDgwluHrJm/AL841+5G1vrHhx88FfW9n/1jwFlDdrpmF9CyJ6jH6j0X0pjlSH0cL4dRCrtP9pf4ghHn8jFEPtkkXTzEIumcbUHmFDgim4eQtwxfbfD9jl6MIyOsImZIX4SPye4k0H7PD3xkzfwmHPdey1DRLSi040klSk0mkVNRIzWvWHtddE3H7Tmy2rxYqwCdLd1EZFctVFxOk6lUoXKzaImIqJv+BpjVVvhcrqvu8yYX0LInkc40MHOwVhl6Ge21+3D+foiDS2t2S7na/rIDavjXwxjnCwSb+axaKTLb6ydTChwRTcXIe54vttgsYPhKIcgbGJ2iJ/EzwnuxO9Uxk8q8JhvdiC86h9tkipczYmI6NvR4ujwzQetmbYq8C+lYxTA3mk207PA3tzYP3A/NPf3RERLL8U/iwEG7nm0A41VhoCj3QuMuOGlNbv1cl6NadJyOW2k4419MYxxski8mceikS6/sXYyocAV3byEuGP4bgfsMk4wjI6wiVkjfhI/J7iTnl2eyvhJBR5zTd1+AYFQMtmciMjeft94olibD1rT3P+1+uHXvQeKUoBMpdPpdDqVvsMHOtg3RLTV871thWN+CSF7jn6gb7wiIiLaN2KXoZc9kqg3XIWX1ny0oRsiWq7Y7DSupEc54PgXQ/yTRfLNPBaNdPmNt5PJBK7o5iHEHdd3GyReMIyOsIlZI34OQvyMsROf0xo/mYUec23oBBTG/oHIwJsz+uaD1nQ+H3Sg8AIMZN2sXqr9bTkt3XrZnYMyV7z6jfG+hJA9Xyn4ZrcM+bp+odoxfvOLuGXoYbY3dBERrXilN1wNKe2yW+7Rovz4F0P/mjg9Zh6LIh5o1MONZpTA1RNeQoxd2gmEuGgHmthOPOIHw+gIm5gt4qcI8XNiO/E4vfGTN/CYZ1a0C+wAs5TWRMIb66JvPmhNT7jtO1CEAgwu2aMtQ8TX2mYdSq/556DUW7XbDyOexaCDDdxzqeyd3TLk6zJ/+6WIiHz52/4DxfkerA5PAS2mQ0qbKlQqI4Ze/37HuBj618TpMfNYFPFAEynt4P2OErj84WWapZ1AiJvddxs7GEY/BGETM0b8FCF+TmwnHqc4flKBB46dO9+Gp8XQbX/TitVmp9PpdDrNak4TkS9FRH4b+3Ahezb0WkBSk6mzBvsEtJjOY2kBiIweuI7thp2HMsRFMAROBeLnFJzm+EkFHjhm3XrQdJn2u/5ctdNw2vpSmUqjeflFERH55X7M4w3e84xmB7Z7PAW0mM5haQGIjB64ju+GnYcyxEQwBE4F4ucUnO74SQUeOE5mu6wSUfROl5kqNILnPUl96z+8ICLyxf/zUbxDDt7zbGYHDuhF5ivUfJUWQKzAdXw37DyUIR6CIXAKED+n4pTHTyrwwLHp1stWJopisxHQYDjA154TEZHP/9+Jd/iZxdDE8JAbhoGUwCzEDFzzcMPOQxkGIxgCJx7xc0pOe/ykAo95lkovD1xmDW9ZTg+Oh9E3H7Sm5/O+A0UogIfZreftJthRgrhI6vf+/cBlo5Vh2IEGf12p554XEZEXf6//QCOWwVp99JA7pvEvhv41cXrMPBZFPNCohxsmfuCK7tjuuDn7bmcWDKMjbGIiiJ/EzwnuxLf6qY2fVOAx11Q7mbF/MGiFwDkbY2w+aE2roW7wgcILYDHb5ZKVqLI6ehD/vRefFxH57b8NWiFSGSII+bp+96siInI0cNuoZbCmHJlByB3/YuhfE6fHzGNRxANNcCdjBq7oju2Om6PvVmYZDKMjbGIiiJ/EzwnuRIT4SQUe881qlQoYqmLdu+FNddE3H7RmKq1qrvLV3gNFKoCICuKq/1Su2qkMyEVhtsv5fD4fNC+m+cnBlyIiX/y6786OUoawPasOSNrqebV9yNf1L5+IiIjxL/F+Ee4hQ3JljFjaUY1/MYx6sjhJZh6LRrr8JrCTsQNX9Bt2vNJOJsQd63erCje7YBgdYRMTQfwcsBLxM85OhPhJBR7zLpNVk01s+G9CZ+7JIW1v0TcftOb+p+qHz/ZjFUC6dXf0U99cGq7U+dUB82J277WsIjyO9SWE7rkniA35ukIWRW0EHdZFapTSjmr8i2HEk8WJMvNYNNLlN+5OJhG4ot+wY5V2giHueL5byyyDYXSETUwE8TMA8TPuToifVOAx9zJXrIwPpXpXXdZm12nZDEr9GHfzQWtatefDWAVw7r3qsP5TVrARvVa2jyOm2a6XVQ+sVxZjfwlD9uzLvxH2dS2dW4pdBtuwNtPRSjuq8S+GkU4WJ8zMY9FIl99YO5lY4Ip6w45V2kmFuOP5bm2zDYbRETYxEcTPAMTPuDshfsrC0dHgUa3AXDC7dWsgkU+u6k8PYfdY6p0rJOLmIWu+XZXbsQrQrecDtvPyFbZbt1KN9B2oWVk6GOdLCNtzT9+ukK9rabxfhDhfSMB3718rWmmtAwVNDjO9iyG86DjZZh6LRrr8Ypd2goFrQNfRSZZ21DLM9rv1lHiSwTA6wiZmhfhpIX4SP8e/bnkDj/mXylQ6zWpO0+wPNC1XbUYOFtE3H7TmpZgFsJoII8tUGs1q0XMc50CpMb+EsD33CDnQmL+IyEYo7ajGvxh4DD3NZh6LRrr84u5kkoFr+qUduQwz/W5HNcVgGB1hExNB/AxC/Iy5kwhOdPzkDTwAAAAAAAnAG3gAAAAAABKACjwAAAAAAAlABR4AAAAAgASgAg8AAAAAQAJQgQcAAAAAIAGowAMAAAAAkABU4AEAAAAASAAq8AAAAAAAJAAVeAAAAAAAEoAKPAAAAAAACUAFHgAAAACABKACDwAAAABAAlCBBwAAAAAgAajAAwAAAACQAFTgcTo922w0rl+4cGHB48KFC9evNzafPTvWkmxetw/fmNyBn236T+7ChcbmxHY+tqmcMgAPQtwMEeKARCN+zhDxMxIq8Dhtnm1ev7CwcPbSjRt3t7e3vUu2t7fv3r1x6ezZhQvXpxA2nm02rl84jni0ef3C2Uv+k9sWOTv14/Y7tlMGYCPEHR9CHHCyED+PD/FzHFTgcao8a1w4e+nu9rDVtu/eODvZ5s7GhYWzl24MP/L4Nq8HneDKq2emf2ifYzxlABZC3LEhxAEnDPHz2BA/x0UFHqfI5vWzN+xgkc1eu/Xg6dMjj6dPnz64dS1rr719462JxeenTwYGqYt3rMM/LE8igD77eNf+MXvrgXN+dy5OYN8jOb5TBqAQ4o4RIQ44UYifx4j4OS4q8Dg1njV+eNf6MXvr6cOHd8oXz/iCw5kzZy6W7zx8+uCa9cEk4/NxcYPitXfKFwl+wKlBiAOAeIifSBQq8Dgtnv3dPStuZW+9H9asd+binae3rEbW7Xt/l7TwbMu+NoshTQBmhBAHAPEQP5EsVOBx6mSvfHtYq+OZb1+xw/OTp1MvEABMDiEOAOIhfiIZjoDTwWkylWsPJrC3B7duXctmvfdSwJAp71F7ZG9Zazq9sZxP/Ie5lvUdJht4mJADBZxw1MIHl6Z3o8gliXbKR0dPnz7wn3TwcXr2ZZ1kTxnDNgVOFELcyIUPLs2w0EOIA04c4ufIhQ8uzbC4RvycECrwODWc+1jk2jg37NMH1wYHQpGsLxbGD1VDDiNZ/0lEjM4jFd63+8Hb9RZ8nOgcdhrZoF+bNzoPPrfgPwPAiUKIG7Xwvt0T4oBTjPg5auF9uyd+Hjcq8Dg9/Hd+lPbEAJ4YPziMuLEgbqiKchh/2I0SnUcsfJR9928UPzpHKF5f6dx9ZcP/mCUxPgMjIcQR4gDEQ/wkfiYJFXicKoH3fzZ77dqtB5FCtWf77DXfFr70IiExpC9IBC3yBLiewxw9ffrAE/2i7W68wnujra+3ka+pdtxT9v9uvMd56m8SDo/ogws4kW5xwJwjxI1eeEIcgKMj4mecwhM/Z4QKPE6bsJ4+6vYePCLGDQXBt/rAiDRaqBo6FitkhcFHill4d7PAEVhOScY7Zd8fpMCRXt7ebYHFC94wvPjAyUOII8QBiIf4SfxMBmahx2lzpnzn4dFTX4Oi1/bdGzcunV24cL2x2ZcdZPO+lSU0e+sHF4M2vng5Urem6HbvbwZ9fKb80LqD7wSWI0jMwjubybV3AjKrnCm/Y201XjqVzfduhGZwOXPxjhNn7wZ/KcEFdM+K2WJxOhDiCHEA4iF+Ej+TgQo8TqUzZy6W7zw8OlL9jW5du9Y7Omb77o1LZy80/PHm4h0rJj4MSxIqMm4kOPPqilOMSwsLF65fb2xuPhsz22i8wg+N6SPsN8Szj3etnwJDrDrQ5fDwfO1yYAHPvjZsdBZw8hDiQhDiAIQgfoYgfs4HKvA45c6cuVgu37nz8KEVqb1xevvG2evBLXlez549e7a52Whcv35h4dLdoatHcfEH3lk+tu/evXHp0tmzCwsLCxcuXG8ENPzGFrnwK6/Gj73DPX1iN66+dnbgSm543v24/xsI2xI4vQhxhDgA8RA/iZ9zigo84FCNrg+9M1vc/WGjPxA8e7bZuH7hwoWFhYWFhYWzZ8+evXTpxo27d7cnV5Lyw+CZOret/lsLwT24Ihip8G6zJ4DEI8T1rUyIAxAJ8bNvZeLn7FCBx6nwrGFFo4UI7aVy5uKd9+3Y2DNu59nm9QsLZ89eunF3e3tywTiwEOWHPXN/+gX24Ap3fIUHcKwIcSKEOABxED9FiJ8JQwUep4I7ZGjQ9BYDN/DavH72Uk87ZDabzV67du2WSjISLTdndD3jsPoSWW7feCtyfD72wk+a29Y73c5aQPIQ4ghxAOIhfhI/E4cKPE4Hd56KiOE5wLPGD+0hQG6SzIcPHz68c+dOuXzx4pkphgw1DuvhQxWqPXk+o87sGa/w7h+poDFF9p5HaboO5vx6wqZ2cUZBAehFiCPEAYiH+En8TBoq8Dgdznz7SuiYpR6eWOY25bnTaNx6/05gOHMTaoxjaLg7c7H8cNS20JiFd2YF2b7xXnBhnv3dPWvPA+b4jML99Qz+7XiylcQ/EHBCEeIIcQDiIX4SP5OGCjxOCTcbpWzfOHshbHaPZ5uNt+yUk55IMGy2jmeNC5OZYTRKqBp16pDYhfdk5ggsjJudc6yY6Z5zcJevZ5vXL52y4AyMhBAXvpwQB2AQ4mf4cuLn/DkCTo2eJsnstVsPnlo9hY6OjvpzhIhcexC8udvFyNqudx4R34ZHR+58odlbT31L3L16lninF1Wl9O7Ld7SeAwXubrzC9xXGWfDAu6T3vEY8Zf9vx3ucngIOOq++o/SVo/erAk4aQlycwhPiABA/4xWe+DkjVOBxqjwdpVtRSMAZdduALe1YERxeIh6qPyQNjFbxCx9h04DQOPIp9/31jHSgExydgdER4uLslRAHgPgZq/DEz9mgCz1OlTMX7xz1Nyf2y1679eDpw3LPMKAz5ffD4lT22gOnwbFnpg1P7yzb4Ck/1AYPnw5MD+Icrr+IYXuMW3g5U34Y9qVlbwWVY+RTFrl45yjsdzPiCQOnECEuZG+EOAAhiJ8heyN+zplZtyAAs2Hn3PAFg2y2rzdS4Ja+TlS+jUIa854+8G92S60Q2j5o9djqKWRYGYc0N8Yt/ICNvd2lAjcY/ZTtw3i6XoUd5wQ3rwLjIMQR4gDEQ/wkfs65haOjIwEAAAAAAPONLvQAAAAAACQAFXgAAAAAABKACjwAAAAAAAlABR4AAAAAgASgAg8AAAAAQAJQgQcAAAAAIAGowAMAAAAAkABU4AEAAAAASAAq8AAAAAAAJAAVeAAAAAAAEoAKPAAAAAAACUAFHgAAAACABKACDwAAAABAAlCBBwAAAAAgAajAAwAAAACQAFTgAQAAAABIACrwAAAAAAAkABV4AAAAAAASgAo8AAAAAAAJQAUeAAAAAIAEoAIPAAAAAEACUIEHAAAAACABqMADAAAAAJAAVOABAAAAAEgAKvAAAAAAACQAFXgAAAAAABKACjwAAAAAAAlABR4AAAAAgASgAg8AAAAAQAJQgQcAAAAAIAGowAMAAAAAkABU4AEAAAAASAAq8AAAAAAAJAAVeAAAAAAAEoAKPAAAAAAACUAFHgAAAACABKACDwAAAABAAlCBBwAAAAAgAajAAwAAAACQAFTgAQAAAABIACrwAAAAAAAkABV4AAAAAAASgAo8AAAAAAAJQAUeAAAAAIAEoAIPAAAAAEACUIEHAAAAACABqMADAAAAAJAAVOABAAAAAEgAKvAAAAAAACQAFXgAAAAAABKACjwAAAAAAAlABR4AAAAAgASgAg8AAAAAQAJQYTenQAAACPdJREFUgQcwVc8aFxYWFhYWFq5vDl3pQuPZ8RXsWDzb3DxppzRVJ/NqebbZuG6dl1vuwA+ncWz/Fbh5PVnf3WRxP06SupaG6r3YuB3mBbcDkFhU4AEcj7uXwiplJ8+zzesXFs5euv901gUZ3Z9+OOsSnKSrZfP62Us37m47/1959cyADycswVfgFPBtzAduh7nAtwEkGxV4AMflBFXKInh63/NAmiCq9j77OvxJuVo2798VEZFrD54eHR0dHR3duTjgw0kLvAIv3jk6Ojp6WJ58BWnOJfV+nH/XHhyF8l1s3A7zgdsBSLZ/N+sCADhF7l66fnkqD2c4gU7U1XLt8sX+SkLgh8Awn//pnw5a9OJPfnKcJYmJ2wEAxsAbeODkW9i5rv7NsAzZa9eyIifnveoJ5X3xPsOX8FwtAAAAgajAAydZT719ltX4137w/q0YlbJnm43rF5zJjS5cuN4Yadqdns2vbz6TgImLrMmY+ss1aMGQUm1eX1hYuKR6hd691LcL/9ZxTuvEm97V4vmVPtv0XRuj/wqiXAY914Gl50PPOUa+4J+FFX/wFei7+MOnA1RL/Qu5HxETt0P/LrkdAMQUPnQJQKLJP13r/3e8RXiqKmLZW0+dn/tHTXpX8nz64Fo2KGxlhw26tARvfu3Bg2s9B1MfBIzlDFoQoVTWZv6j+s50nNOamj/57wH/jtf0rxb7V3qt/3c0fDSvf0cxLoMB7G0iX/DBl1GUK9B/8Qd/lUcBa45UvFG+Me7HGP6/P/mTQf+OsRQDf09ha0fA7cDtACAcFXjgxAqsvR97Hd7/TDSgUhb04OSsmr1lT23kPpwEPmIFHVcke83d3Pt4Eu8JKXqpAp+urK3dMo14VlM1ZxX46VwtnqdX75VhfxrxKTXo6np6yzqe/9cYeHGFXRzD92mX1lnRKb9vhyFHdnZnH3TQte8ed4RTjvqNcT/GlcQKfMg23A5H3A4ARkEFHjix5rECP6BSFlAlsx4w+h4aBj9i+Qza3K3AxXpCGqFUATsd+IIn4klNU2Dt/djr8NO/WgZV1QdeMEGGXV3efUetsUTfZ9geA76U0BrLwEtPrTf8yo9Yi+N+nKi5qsCH6/l2uR2ibM7tACAcY+ABHKsz5Uijm63EQtfe6U3wc6b8zjURkbv3w8ZGD9xcLv5gcC/BocYs1Znyw6OjoKRFZ15diV2mE21qV0v21g/809uffS3yZfGs8cMhV1f4ZTDmPu2Tvew7gYuXr4mIbD8ZMbHzgG9IHcQ5xrinzP2IUXA7xMHtAJweVOCBE+vo3J2RPj8uUSplzz7eFRHJvna2f5lV1dr9ePC8OmGbn/n2lbiPSGOWqndvz54929xsNK5fuGBPKTQr4RPOzzQn/JSulpVXe59x1UNqlCf+p0+2RfqqDL7djHAZjLrPQSerMlrHSLtn1XW8D/dWBcUtzZinzP14koW/mI2VY53bIQZuB+AUIQ88gGN3pvz+rXtnb2wPzvRtPSH1VbNErCekbVXVCn82DNx8DJMo1bPNxns/vHF3e6IFG9dPvjXrEoQ4rqvF51njwtkb/b+kaw/cAty9tDDwuXbUw9ki7DPsZOO5ePma3L0rd+9v3rl4UUTk2d/d25ag+smYp8z9iJFwO4yC2wE4RajAAyeZetnupI6b9bt315nyO9duXLorVqUs4JXBFI+tnmVm4dnm9bPu24xsNisrK1cuX/722Y/fCqouQpnl1XIa9FRZVIWlb5DBtHA/ju7Fn/xk1kU4ubgdRCRRtwNwCtGFHjj5js7dUf9mXRCvi3esaXTu/jAg7W5Yj7+wnoJ+wR0Gre0j6F1z3FJtvqcej6xJfh8+fPjwzp3yxYuTfRFzEh3H1eJjjQft5X3/H9pzePSuu1H3OXpn2OF83YY337uxLZK98u3+y3LMU+Z+xEi4HcJwOwCnGBV4ADNiV8q2b7z13pOeZWEjkiN0mrQGEm7f+7uAZxlr+35DHwLHLJU9x9CDO73PRAOLBNsUr5ZRTaPOEH2fA0928/rCwsLCQthkfwN5qizqKu2psIx5ytyPGAm3Qz9uBwAuKvAAZsaplN3tG3RnP0L1vXB1JgAOmkDIYT8i3Xiv9/nN3t7Leh7rffJR714mWKoBgoqEPtO7WkY0+OISeda4sLCwsHAhoJ/ApPYZMMuWiPP8PWpXA4szefZ1Nd92z0TW454y9yNGwe3gwe0AoF9IByAAGNvA5LIWby5h30pOCvDsrQd2VvAH17IBa4bvOHvt1tO+7Xt24UkOHrSqp6tk9FL15+T1JDV30g77jkOi3elfLdGTKofxpGt+4P5+nQPGyQM/wj7tk83aF9LTB7cGH9n/ZfYmvu7b6aAvYYRTDsT9eOKMdMuEbcPt4N+a2wHAEFTgAUzVsCqZt1LWs5L/6cGVdZ8wwg8duH02mw0okbdm6D6vBD1YRi3VU19CX7WLp8FJfrO3Hgz9lk6H6V8tk6nADz5cX7ki11hG2OeACynr31/QFTi4xuJZfcB3EL14o3xl3I+JFfRLCuT5hXE7hG/P7QAgCirwAKZqeJXM8zYi4EXIg1vXss5jRTZ77Va0yrtve8/mD54OKpF/TXWcQZW6iKV6cMtZyTna0weBWwa+Hjp9pn+1TKoC33+4AdfBCDWWyPu01vSfa9Bq/Vfg4BpLpC8/evFCtud+PBmmXIE/OuJ2GLAmtwNw2i0cHR1FjMAAcBJYKb6zt54+LDPBLjBb3I+Ag9sBQARMYgcAAAAAQAJQgQcAAAAAIAGowAMAAAAAkABU4AEAAAAASAAmsQMAAAAAIAF4Aw8AAAAAQAJQgQcAAAAAIAGowAMAAAAAkABU4AEAAAAASAAq8AAAAAAAJAAVeAAAAAAAEoAKPAAAAAAACUAFHgAAAACABKACDwAAAABAAlCBBwAAAAAgAajAAwAAAACQAFTgAQAAAABIACrwAAAAAAAkABV4AAAAAAASgAo8AAAAAAAJQAUeAAAAAIAEoAIPAAAAAEACUIEHAAAAACABqMADAAAAAJAAVOABAAAAAEgAKvAAAAAAACQAFXgAAAAAABLg/weFVXwjQC/+cQAAAABJRU5ErkJggg==" width="672" /></p>
<div class="sourceCode" id="cb259"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb259-1"><a href="#cb259-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_A3.pdf&quot;</span>, <span class="at">width =</span> <span class="fl">8.5</span>, <span class="at">height =</span> <span class="fl">13.5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb260"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb260-1"><a href="#cb260-1" tabindex="-1"></a><span class="fu">ggarrange</span>(Euro_plot_4, Euro_plot_5, Euro_plot_6, <span class="at">ncol=</span><span class="dv">3</span>, <span class="at">nrow=</span><span class="dv">1</span>, <span class="at">common.legend =</span> <span class="cn">TRUE</span>, <span class="at">legend=</span><span class="st">&quot;bottom&quot;</span>) </span></code></pre></div>
<p><img 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" width="672" /></p>
<div class="sourceCode" id="cb261"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb261-1"><a href="#cb261-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_A4.pdf&quot;</span>, <span class="at">width =</span> <span class="fl">8.5</span>, <span class="at">height =</span> <span class="fl">13.5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
</div>
<div id="latinobarometer-dotchart" class="section level4">
<h4>Latinobarometer dotchart</h4>
<div class="sourceCode" id="cb262"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb262-1"><a href="#cb262-1" tabindex="-1"></a>Latino_analysis <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Analysis Data/Latino_analysis.rds&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb263"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb263-1"><a href="#cb263-1" tabindex="-1"></a>Latino_analysis <span class="ot">&lt;-</span> Latino_analysis <span class="sc">%&gt;%</span></span>
<span id="cb263-2"><a href="#cb263-2" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">quota_effect =</span> <span class="fu">case_when</span>(</span>
<span id="cb263-3"><a href="#cb263-3" tabindex="-1"></a>                           quota_effect <span class="sc">==</span> <span class="st">&quot;No Quota&quot;</span> <span class="sc">~</span> <span class="st">&quot;No quota&quot;</span>,</span>
<span id="cb263-4"><a href="#cb263-4" tabindex="-1"></a>                           quota_effect <span class="sc">==</span> <span class="st">&quot;Non-Effective&quot;</span> <span class="sc">~</span> <span class="st">&quot;Non-effective quota&quot;</span>,</span>
<span id="cb263-5"><a href="#cb263-5" tabindex="-1"></a>                           quota_effect <span class="sc">==</span> <span class="st">&quot;Effective&quot;</span> <span class="sc">~</span> <span class="st">&quot;Effective quota&quot;</span>))</span>
<span id="cb263-6"><a href="#cb263-6" tabindex="-1"></a>                </span>
<span id="cb263-7"><a href="#cb263-7" tabindex="-1"></a></span>
<span id="cb263-8"><a href="#cb263-8" tabindex="-1"></a></span>
<span id="cb263-9"><a href="#cb263-9" tabindex="-1"></a></span>
<span id="cb263-10"><a href="#cb263-10" tabindex="-1"></a>Latino_agg <span class="ot">&lt;-</span> Latino_analysis <span class="sc">%&gt;%</span></span>
<span id="cb263-11"><a href="#cb263-11" tabindex="-1"></a> <span class="fu">group_by</span>(Country_year, quota_effect)<span class="sc">%&gt;%</span></span>
<span id="cb263-12"><a href="#cb263-12" tabindex="-1"></a>  <span class="fu">summarize_at</span>(<span class="fu">vars</span>(<span class="at">Satisfaction =</span> Satisfaction), </span>
<span id="cb263-13"><a href="#cb263-13" tabindex="-1"></a>                                   mean, <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
<span id="cb263-14"><a href="#cb263-14" tabindex="-1"></a></span>
<span id="cb263-15"><a href="#cb263-15" tabindex="-1"></a></span>
<span id="cb263-16"><a href="#cb263-16" tabindex="-1"></a></span>
<span id="cb263-17"><a href="#cb263-17" tabindex="-1"></a>Latino_agg<span class="sc">$</span>quota_effect <span class="ot">&lt;-</span> <span class="fu">factor</span>(Latino_agg<span class="sc">$</span>quota_effect , <span class="at">levels =</span> <span class="fu">c</span>(<span class="st">&quot;No quota&quot;</span>, <span class="st">&quot;Non-effective quota&quot;</span>, <span class="st">&quot;Effective quota&quot;</span>))</span>
<span id="cb263-18"><a href="#cb263-18" tabindex="-1"></a></span>
<span id="cb263-19"><a href="#cb263-19" tabindex="-1"></a>Latino_agg <span class="ot">&lt;-</span> Latino_agg[<span class="fu">order</span>(Latino_agg<span class="sc">$</span>Country_year),]</span>
<span id="cb263-20"><a href="#cb263-20" tabindex="-1"></a></span>
<span id="cb263-21"><a href="#cb263-21" tabindex="-1"></a></span>
<span id="cb263-22"><a href="#cb263-22" tabindex="-1"></a>Latino_agg1 <span class="ot">&lt;-</span> Latino_agg[<span class="dv">1</span><span class="sc">:</span><span class="dv">102</span>,]</span>
<span id="cb263-23"><a href="#cb263-23" tabindex="-1"></a>Latino_agg2 <span class="ot">&lt;-</span> Latino_agg[<span class="dv">103</span><span class="sc">:</span><span class="dv">205</span>,]</span>
<span id="cb263-24"><a href="#cb263-24" tabindex="-1"></a>Latino_agg3 <span class="ot">&lt;-</span> Latino_agg[<span class="dv">206</span><span class="sc">:</span><span class="dv">319</span>,]</span></code></pre></div>
<div class="sourceCode" id="cb264"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb264-1"><a href="#cb264-1" tabindex="-1"></a>Latino_agg1 <span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(Latino_agg1[<span class="fu">order</span>(<span class="fu">rev</span>(Latino_agg1<span class="sc">$</span>Country_year), <span class="at">decreasing =</span> <span class="cn">TRUE</span>), ])</span>
<span id="cb264-2"><a href="#cb264-2" tabindex="-1"></a></span>
<span id="cb264-3"><a href="#cb264-3" tabindex="-1"></a>Latino_plot_1 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(Latino_agg1, <span class="fu">aes</span>(<span class="at">x =</span> Satisfaction, <span class="at">y =</span> <span class="fu">reorder</span>(Country_year, <span class="fu">desc</span>(Country_year)))) <span class="sc">+</span></span>
<span id="cb264-4"><a href="#cb264-4" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">shape=</span>quota_effect, <span class="at">colour =</span> quota_effect), <span class="at">size =</span> <span class="fl">1.3</span>, <span class="at">position=</span><span class="fu">position_dodge</span>(<span class="at">width=</span><span class="dv">0</span>)) <span class="sc">+</span> </span>
<span id="cb264-5"><a href="#cb264-5" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>)) <span class="sc">+</span></span>
<span id="cb264-6"><a href="#cb264-6" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="dv">10</span>) <span class="sc">+</span></span>
<span id="cb264-7"><a href="#cb264-7" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.y =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="fu">rel</span>(.<span class="dv">7</span>)),</span>
<span id="cb264-8"><a href="#cb264-8" tabindex="-1"></a>        <span class="at">axis.ticks.y =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb264-9"><a href="#cb264-9" tabindex="-1"></a>        <span class="at">axis.title.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="fu">rel</span>(.<span class="dv">9</span>)),</span>
<span id="cb264-10"><a href="#cb264-10" tabindex="-1"></a>        <span class="at">panel.grid.major.x =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb264-11"><a href="#cb264-11" tabindex="-1"></a>        <span class="at">panel.grid.major.y =</span> <span class="fu">element_line</span>(<span class="at">size =</span> <span class="fl">0.4</span>),</span>
<span id="cb264-12"><a href="#cb264-12" tabindex="-1"></a>        <span class="at">panel.grid.minor.x =</span> <span class="fu">element_blank</span>(), </span>
<span id="cb264-13"><a href="#cb264-13" tabindex="-1"></a>        <span class="at">legend.position =</span> <span class="st">&quot;bottom&quot;</span>, </span>
<span id="cb264-14"><a href="#cb264-14" tabindex="-1"></a>        <span class="at">legend.title =</span> <span class="fu">element_blank</span>()) <span class="sc">+</span></span>
<span id="cb264-15"><a href="#cb264-15" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;springgreen3&quot;</span>,<span class="st">&quot;steelblue1&quot;</span>,<span class="st">&quot;indianred2&quot;</span>)) <span class="sc">+</span></span>
<span id="cb264-16"><a href="#cb264-16" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Satisfaction&quot;</span>) <span class="sc">+</span></span>
<span id="cb264-17"><a href="#cb264-17" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;&quot;</span>)</span>
<span id="cb264-18"><a href="#cb264-18" tabindex="-1"></a></span>
<span id="cb264-19"><a href="#cb264-19" tabindex="-1"></a></span>
<span id="cb264-20"><a href="#cb264-20" tabindex="-1"></a>Latino_agg2 <span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(Latino_agg2[<span class="fu">order</span>(<span class="fu">rev</span>(Latino_agg2<span class="sc">$</span>Country_year), <span class="at">decreasing =</span> <span class="cn">TRUE</span>), ])</span>
<span id="cb264-21"><a href="#cb264-21" tabindex="-1"></a></span>
<span id="cb264-22"><a href="#cb264-22" tabindex="-1"></a>Latino_plot_2 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(Latino_agg2, <span class="fu">aes</span>(<span class="at">x =</span> Satisfaction, <span class="at">y =</span> <span class="fu">reorder</span>(Country_year, <span class="fu">desc</span>(Country_year)))) <span class="sc">+</span></span>
<span id="cb264-23"><a href="#cb264-23" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">shape=</span>quota_effect, <span class="at">colour =</span> quota_effect), <span class="at">size =</span> <span class="fl">1.3</span>, <span class="at">position=</span><span class="fu">position_dodge</span>(<span class="at">width=</span><span class="dv">0</span>)) <span class="sc">+</span> </span>
<span id="cb264-24"><a href="#cb264-24" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>)) <span class="sc">+</span></span>
<span id="cb264-25"><a href="#cb264-25" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="dv">10</span>) <span class="sc">+</span></span>
<span id="cb264-26"><a href="#cb264-26" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.y =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="fu">rel</span>(.<span class="dv">65</span>)),</span>
<span id="cb264-27"><a href="#cb264-27" tabindex="-1"></a>        <span class="at">axis.ticks.y =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb264-28"><a href="#cb264-28" tabindex="-1"></a>        <span class="at">axis.title.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="fu">rel</span>(.<span class="dv">9</span>)),</span>
<span id="cb264-29"><a href="#cb264-29" tabindex="-1"></a>        <span class="at">panel.grid.major.x =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb264-30"><a href="#cb264-30" tabindex="-1"></a>        <span class="at">panel.grid.major.y =</span> <span class="fu">element_line</span>(<span class="at">size =</span> <span class="fl">0.4</span>),</span>
<span id="cb264-31"><a href="#cb264-31" tabindex="-1"></a>        <span class="at">panel.grid.minor.x =</span> <span class="fu">element_blank</span>(), </span>
<span id="cb264-32"><a href="#cb264-32" tabindex="-1"></a>        <span class="at">legend.position =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span></span>
<span id="cb264-33"><a href="#cb264-33" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;springgreen3&quot;</span>,<span class="st">&quot;steelblue1&quot;</span>,<span class="st">&quot;indianred2&quot;</span>)) <span class="sc">+</span></span>
<span id="cb264-34"><a href="#cb264-34" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Satisfaction&quot;</span>) <span class="sc">+</span></span>
<span id="cb264-35"><a href="#cb264-35" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;&quot;</span>)</span>
<span id="cb264-36"><a href="#cb264-36" tabindex="-1"></a></span>
<span id="cb264-37"><a href="#cb264-37" tabindex="-1"></a></span>
<span id="cb264-38"><a href="#cb264-38" tabindex="-1"></a>Latino_agg3 <span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(Latino_agg3[<span class="fu">order</span>(<span class="fu">rev</span>(Latino_agg3<span class="sc">$</span>Country_year), <span class="at">decreasing =</span> <span class="cn">TRUE</span>), ])</span>
<span id="cb264-39"><a href="#cb264-39" tabindex="-1"></a></span>
<span id="cb264-40"><a href="#cb264-40" tabindex="-1"></a></span>
<span id="cb264-41"><a href="#cb264-41" tabindex="-1"></a>Latino_plot_3 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(Latino_agg3, <span class="fu">aes</span>(<span class="at">x =</span> Satisfaction, <span class="at">y =</span> <span class="fu">reorder</span>(Country_year, <span class="fu">desc</span>(Country_year)))) <span class="sc">+</span></span>
<span id="cb264-42"><a href="#cb264-42" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">shape=</span>quota_effect, <span class="at">colour =</span> quota_effect), <span class="at">size =</span> <span class="fl">1.3</span>, <span class="at">position=</span><span class="fu">position_dodge</span>(<span class="at">width=</span><span class="dv">0</span>)) <span class="sc">+</span> </span>
<span id="cb264-43"><a href="#cb264-43" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>)) <span class="sc">+</span></span>
<span id="cb264-44"><a href="#cb264-44" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="dv">10</span>) <span class="sc">+</span></span>
<span id="cb264-45"><a href="#cb264-45" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.y =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="fu">rel</span>(.<span class="dv">65</span>)),</span>
<span id="cb264-46"><a href="#cb264-46" tabindex="-1"></a>        <span class="at">axis.ticks.y =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb264-47"><a href="#cb264-47" tabindex="-1"></a>        <span class="at">axis.title.x =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="fu">rel</span>(.<span class="dv">9</span>)),</span>
<span id="cb264-48"><a href="#cb264-48" tabindex="-1"></a>        <span class="at">panel.grid.major.x =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb264-49"><a href="#cb264-49" tabindex="-1"></a>        <span class="at">panel.grid.major.y =</span> <span class="fu">element_line</span>(<span class="at">size =</span> <span class="fl">0.4</span>),</span>
<span id="cb264-50"><a href="#cb264-50" tabindex="-1"></a>        <span class="at">panel.grid.minor.x =</span> <span class="fu">element_blank</span>(), </span>
<span id="cb264-51"><a href="#cb264-51" tabindex="-1"></a>        <span class="at">legend.position =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span></span>
<span id="cb264-52"><a href="#cb264-52" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;springgreen3&quot;</span>,<span class="st">&quot;steelblue1&quot;</span>,<span class="st">&quot;indianred2&quot;</span>)) <span class="sc">+</span></span>
<span id="cb264-53"><a href="#cb264-53" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">&quot;Satisfaction&quot;</span>) <span class="sc">+</span></span>
<span id="cb264-54"><a href="#cb264-54" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb265"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb265-1"><a href="#cb265-1" tabindex="-1"></a><span class="fu">ggarrange</span>(Latino_plot_1, Latino_plot_2, Latino_plot_3, <span class="at">ncol=</span><span class="dv">3</span>, <span class="at">nrow=</span><span class="dv">1</span>, <span class="at">common.legend =</span> <span class="cn">TRUE</span>, <span class="at">legend=</span><span class="st">&quot;bottom&quot;</span>) <span class="co">#5.5x7 inches</span></span></code></pre></div>
<p><img 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" width="672" /></p>
<div class="sourceCode" id="cb266"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb266-1"><a href="#cb266-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_A5.pdf&quot;</span>, <span class="at">width =</span> <span class="fl">8.5</span>, <span class="at">height =</span> <span class="fl">13.5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
</div>
</div>
<div id="tables-for-regression-models" class="section level3">
<h3>Tables for regression models</h3>
<p>Tables for Study 1 Hypothesis 1-Hypothesis 4. Note that there are
adjustments made in latex after generating the base latex code using the
<code>broom.mixed</code> and <code>stargazer</code> packages.</p>
<div id="base-table-table-a1" class="section level4">
<h4>Base table-Table A1</h4>
<div class="sourceCode" id="cb267"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb267-1"><a href="#cb267-1" tabindex="-1"></a><span class="co">#bring in the loaded models </span></span>
<span id="cb267-2"><a href="#cb267-2" tabindex="-1"></a>Americas_hyp1 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Americas_hyp1.rds&quot;</span>)</span>
<span id="cb267-3"><a href="#cb267-3" tabindex="-1"></a>CSES_hyp1 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;CSES_hyp1.rds&quot;</span>)</span>
<span id="cb267-4"><a href="#cb267-4" tabindex="-1"></a>Euro_hyp1 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Euro_hyp1.rds&quot;</span>)</span>
<span id="cb267-5"><a href="#cb267-5" tabindex="-1"></a>Latino_hyp1 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Latino_hyp1.rds&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb268"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb268-1"><a href="#cb268-1" tabindex="-1"></a>Americas_hyp1.mod <span class="ot">&lt;-</span> broom.mixed<span class="sc">::</span><span class="fu">tidy</span>(Americas_hyp1) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(effect <span class="sc">==</span> <span class="st">&quot;fixed&quot;</span>)</span>
<span id="cb268-2"><a href="#cb268-2" tabindex="-1"></a>CSES_hyp1.mod <span class="ot">&lt;-</span> broom.mixed<span class="sc">::</span><span class="fu">tidy</span>(CSES_hyp1) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(effect <span class="sc">==</span> <span class="st">&quot;fixed&quot;</span>)</span>
<span id="cb268-3"><a href="#cb268-3" tabindex="-1"></a></span>
<span id="cb268-4"><a href="#cb268-4" tabindex="-1"></a>Euro_hyp1.mod <span class="ot">&lt;-</span> broom.mixed<span class="sc">::</span><span class="fu">tidy</span>(Euro_hyp1) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(effect <span class="sc">==</span> <span class="st">&quot;fixed&quot;</span>)</span>
<span id="cb268-5"><a href="#cb268-5" tabindex="-1"></a>Latino_hyp1.mod <span class="ot">&lt;-</span> broom.mixed<span class="sc">::</span><span class="fu">tidy</span>(Latino_hyp1) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(effect <span class="sc">==</span> <span class="st">&quot;fixed&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb269"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb269-1"><a href="#cb269-1" tabindex="-1"></a><span class="co"># Number of unique surveys for that one random effect</span></span>
<span id="cb269-2"><a href="#cb269-2" tabindex="-1"></a>Americas_hyp1_num_survey <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(<span class="fu">sapply</span>(lme4<span class="sc">::</span><span class="fu">ranef</span>(Americas_hyp1),nrow)[<span class="dv">1</span>])</span>
<span id="cb269-3"><a href="#cb269-3" tabindex="-1"></a>CSES_hyp1_num_survey <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(<span class="fu">sapply</span>(lme4<span class="sc">::</span><span class="fu">ranef</span>(CSES_hyp1),nrow)[<span class="dv">1</span>])</span>
<span id="cb269-4"><a href="#cb269-4" tabindex="-1"></a>Euro_hyp1_num_survey <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(<span class="fu">sapply</span>(lme4<span class="sc">::</span><span class="fu">ranef</span>(Euro_hyp1),nrow)[<span class="dv">1</span>])</span>
<span id="cb269-5"><a href="#cb269-5" tabindex="-1"></a>Latino_hyp1_num_survey <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(<span class="fu">sapply</span>(lme4<span class="sc">::</span><span class="fu">ranef</span>(Latino_hyp1),nrow)[<span class="dv">1</span>])</span></code></pre></div>
<div class="sourceCode" id="cb270"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb270-1"><a href="#cb270-1" tabindex="-1"></a><span class="co"># variance for survey for base models</span></span>
<span id="cb270-2"><a href="#cb270-2" tabindex="-1"></a>var_Americas_hyp1.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_random</span>(Americas_hyp1)), <span class="dv">3</span>) </span>
<span id="cb270-3"><a href="#cb270-3" tabindex="-1"></a></span>
<span id="cb270-4"><a href="#cb270-4" tabindex="-1"></a>var_CSES_hyp1.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_random</span>(CSES_hyp1)), <span class="dv">3</span>) </span>
<span id="cb270-5"><a href="#cb270-5" tabindex="-1"></a></span>
<span id="cb270-6"><a href="#cb270-6" tabindex="-1"></a>var_Euro_hyp1.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_random</span>(Euro_hyp1)), <span class="dv">3</span>) </span>
<span id="cb270-7"><a href="#cb270-7" tabindex="-1"></a></span>
<span id="cb270-8"><a href="#cb270-8" tabindex="-1"></a>var_Latino_hyp1.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_random</span>(Latino_hyp1)), <span class="dv">3</span>)</span></code></pre></div>
<div class="sourceCode" id="cb271"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb271-1"><a href="#cb271-1" tabindex="-1"></a>sd_Americas_hyp1.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(Americas_hyp1)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev), <span class="dv">3</span>)</span>
<span id="cb271-2"><a href="#cb271-2" tabindex="-1"></a></span>
<span id="cb271-3"><a href="#cb271-3" tabindex="-1"></a>sd_CSES_hyp1.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(CSES_hyp1)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev), <span class="dv">3</span>)</span>
<span id="cb271-4"><a href="#cb271-4" tabindex="-1"></a>sd_Euro_hyp1.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(Euro_hyp1)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev), <span class="dv">3</span>)</span>
<span id="cb271-5"><a href="#cb271-5" tabindex="-1"></a>sd_Latino_hyp1.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(Latino_hyp1)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev), <span class="dv">3</span>)</span></code></pre></div>
<div class="sourceCode" id="cb272"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb272-1"><a href="#cb272-1" tabindex="-1"></a><span class="co"># AIC for models</span></span>
<span id="cb272-2"><a href="#cb272-2" tabindex="-1"></a>AIC_Americas_hyp1.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">AIC</span>(<span class="fu">logLik</span>(Americas_hyp1))), <span class="dv">3</span>)</span>
<span id="cb272-3"><a href="#cb272-3" tabindex="-1"></a>AIC_CSES_hyp1.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">AIC</span>(<span class="fu">logLik</span>(CSES_hyp1))), <span class="dv">3</span>)</span>
<span id="cb272-4"><a href="#cb272-4" tabindex="-1"></a>AIC_Euro_hyp1.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">AIC</span>(<span class="fu">logLik</span>(Euro_hyp1))), <span class="dv">3</span>)</span>
<span id="cb272-5"><a href="#cb272-5" tabindex="-1"></a>AIC_Latino_hyp1.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">AIC</span>(<span class="fu">logLik</span>(Latino_hyp1))), <span class="dv">3</span>)</span></code></pre></div>
<div class="sourceCode" id="cb273"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb273-1"><a href="#cb273-1" tabindex="-1"></a>hyp1_stats<span class="ot">&lt;-</span> <span class="fu">tribble</span>(<span class="sc">~</span>stat, <span class="sc">~</span>Americas_hyp1, <span class="sc">~</span>CSES_hyp1, <span class="sc">~</span>Euro_hyp1, <span class="sc">~</span>Latino_hyp1,</span>
<span id="cb273-2"><a href="#cb273-2" tabindex="-1"></a><span class="st">&quot;Number of respondents&quot;</span>, <span class="fu">nobs</span>(Americas_hyp1), <span class="fu">nobs</span>(CSES_hyp1), <span class="fu">nobs</span>(Euro_hyp1), <span class="fu">nobs</span>(Latino_hyp1),      </span>
<span id="cb273-3"><a href="#cb273-3" tabindex="-1"></a><span class="st">&quot;Number of surveys&quot;</span>, Americas_hyp1_num_survey, CSES_hyp1_num_survey , Euro_hyp1_num_survey, Latino_hyp1_num_survey,</span>
<span id="cb273-4"><a href="#cb273-4" tabindex="-1"></a>        <span class="st">&quot;var(survey-level constants)&quot;</span>, var_Americas_hyp1.mod,var_CSES_hyp1.mod, var_Euro_hyp1.mod, var_Latino_hyp1.mod,</span>
<span id="cb273-5"><a href="#cb273-5" tabindex="-1"></a>        <span class="st">&quot;sd(survey-level constants&quot;</span>, sd_Americas_hyp1.mod, sd_CSES_hyp1.mod, sd_Euro_hyp1.mod, sd_Latino_hyp1.mod,</span>
<span id="cb273-6"><a href="#cb273-6" tabindex="-1"></a>        <span class="st">&quot;AIC&quot;</span>, AIC_Americas_hyp1.mod,AIC_CSES_hyp1.mod, AIC_Euro_hyp1.mod, AIC_Latino_hyp1.mod,</span>
<span id="cb273-7"><a href="#cb273-7" tabindex="-1"></a>        ) </span></code></pre></div>
<div class="sourceCode" id="cb274"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb274-1"><a href="#cb274-1" tabindex="-1"></a><span class="fu">stargazer</span>(Americas_hyp1, CSES_hyp1, Euro_hyp1, Latino_hyp1, </span>
<span id="cb274-2"><a href="#cb274-2" tabindex="-1"></a>          <span class="at">type=</span><span class="st">&quot;latex&quot;</span>, </span>
<span id="cb274-3"><a href="#cb274-3" tabindex="-1"></a>          <span class="co"># Below: manually supply tidied coefficients and standard errors</span></span>
<span id="cb274-4"><a href="#cb274-4" tabindex="-1"></a>          <span class="at">coef =</span> <span class="fu">list</span>(Americas_hyp1.mod<span class="sc">$</span>estimate ,CSES_hyp1.mod<span class="sc">$</span>estimate, Euro_hyp1.mod<span class="sc">$</span>estimate, Latino_hyp1.mod<span class="sc">$</span>estimate),</span>
<span id="cb274-5"><a href="#cb274-5" tabindex="-1"></a>          <span class="at">se =</span> <span class="fu">list</span>(Americas_hyp1.mod<span class="sc">$</span>std.error, CSES_hyp1.mod<span class="sc">$</span>std.error, Euro_hyp1.mod<span class="sc">$</span>std.error, Latino_hyp1.mod<span class="sc">$</span>std.error),</span>
<span id="cb274-6"><a href="#cb274-6" tabindex="-1"></a>           <span class="at">star.char =</span> <span class="fu">c</span>( <span class="st">&quot;*&quot;</span>),</span>
<span id="cb274-7"><a href="#cb274-7" tabindex="-1"></a>          <span class="at">star.cutoffs =</span> <span class="fu">c</span>( .<span class="dv">05</span>),</span>
<span id="cb274-8"><a href="#cb274-8" tabindex="-1"></a>          <span class="co"># Omit model statistics by default...</span></span>
<span id="cb274-9"><a href="#cb274-9" tabindex="-1"></a>          <span class="at">omit.table.layout =</span> <span class="st">&quot;s&quot;</span>,</span>
<span id="cb274-10"><a href="#cb274-10" tabindex="-1"></a>          <span class="at">add.lines =</span> <span class="fu">lapply</span>(<span class="dv">1</span><span class="sc">:</span><span class="fu">nrow</span>(hyp1_stats), <span class="cf">function</span>(i) <span class="fu">unlist</span>(hyp1_stats[i, ])),</span>
<span id="cb274-11"><a href="#cb274-11" tabindex="-1"></a></span>
<span id="cb274-12"><a href="#cb274-12" tabindex="-1"></a>          <span class="at">model.names =</span> <span class="cn">FALSE</span>,</span>
<span id="cb274-13"><a href="#cb274-13" tabindex="-1"></a>          <span class="at">column.labels =</span> <span class="fu">c</span>(<span class="st">&quot;AmericasBarometer&quot;</span>,<span class="st">&quot;CSES&quot;</span>, <span class="st">&quot;Eurobarometer&quot;</span>, <span class="st">&quot;Latinobarometer&quot;</span>)</span>
<span id="cb274-14"><a href="#cb274-14" tabindex="-1"></a>          )</span></code></pre></div>
<pre><code>## 
## % Table created by stargazer v.5.2.3 by Marek Hlavac, Social Policy Institute. E-mail: marek.hlavac at gmail.com
## % Date and time: Tue, Jul 02, 2024 - 7:56:45 PM
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}}lcccc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
##  &amp; \multicolumn{4}{c}{\textit{Dependent variable:}} \\ 
## \cline{2-5} 
## \\[-1.8ex] &amp; \multicolumn{4}{c}{Satisfaction} \\ 
##  &amp; AmericasBarometer &amp; CSES &amp; Eurobarometer &amp; Latinobarometer \\ 
## \\[-1.8ex] &amp; (1) &amp; (2) &amp; (3) &amp; (4)\\ 
## \hline \\[-1.8ex] 
##  quota\_effectNon-Effective &amp; 0.029 &amp; 0.036 &amp; $-$0.012 &amp; $-$0.026 \\ 
##   &amp; (0.015) &amp; (0.031) &amp; (0.020) &amp; (0.018) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  quota\_effectEffective &amp; $-$0.016 &amp; $-$0.071$^{*}$ &amp; $-$0.080$^{*}$ &amp; $-$0.067$^{*}$ \\ 
##   &amp; (0.016) &amp; (0.022) &amp; (0.015) &amp; (0.018) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  FemaleWoman &amp; $-$0.007$^{*}$ &amp; $-$0.005$^{*}$ &amp; $-$0.005$^{*}$ &amp; $-$0.007$^{*}$ \\ 
##   &amp; (0.001) &amp; (0.001) &amp; (0.001) &amp; (0.001) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Ideology &amp; 0.019$^{*}$ &amp; 0.089$^{*}$ &amp; 0.089$^{*}$ &amp; 0.033$^{*}$ \\ 
##   &amp; (0.003) &amp; (0.003) &amp; (0.001) &amp; (0.002) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Education &amp; $-$0.057$^{*}$ &amp; 0.024$^{*}$ &amp;  &amp; $-$0.0002$^{*}$ \\ 
##   &amp; (0.003) &amp; (0.002) &amp;  &amp; (0.0001) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Income &amp; 0.003 &amp; 0.049$^{*}$ &amp;  &amp;  \\ 
##   &amp; (0.005) &amp; (0.002) &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Age\_education &amp;  &amp;  &amp; 0.047$^{*}$ &amp;  \\ 
##   &amp;  &amp;  &amp; (0.001) &amp;  \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Age &amp; 0.010$^{*}$ &amp; 0.008$^{*}$ &amp; 0.004$^{*}$ &amp; 0.0002$^{*}$ \\ 
##   &amp; (0.004) &amp; (0.003) &amp; (0.002) &amp; (0.00003) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Polity\_score &amp; $-$0.019 &amp; $-$0.033 &amp; $-$0.007 &amp; 0.009 \\ 
##   &amp; (0.049) &amp; (0.074) &amp; (0.019) &amp; (0.005) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  GDP\_capita\_logged &amp; 0.055 &amp; 0.058 &amp; 0.174$^{*}$ &amp; 0.023 \\ 
##   &amp; (0.042) &amp; (0.068) &amp; (0.024) &amp; (0.016) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  women\_rep\_lag &amp; 0.037 &amp; 0.097$^{*}$ &amp; 0.012 &amp; 0.0003 \\ 
##   &amp; (0.040) &amp; (0.035) &amp; (0.020) &amp; (0.001) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Corruption &amp; $-$0.148$^{*}$ &amp; $-$0.193$^{*}$ &amp; $-$0.283$^{*}$ &amp; $-$0.258$^{*}$ \\ 
##   &amp; (0.034) &amp; (0.063) &amp; (0.039) &amp; (0.046) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Presidentialism &amp; 0.069 &amp; 0.013 &amp; $-$0.156 &amp; 0.253$^{*}$ \\ 
##   &amp; (0.035) &amp; (0.096) &amp; (0.107) &amp; (0.048) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Constant &amp; 0.571$^{*}$ &amp; 0.440$^{*}$ &amp; 0.424$^{*}$ &amp; 0.246 \\ 
##   &amp; (0.062) &amp; (0.093) &amp; (0.024) &amp; (0.174) \\ 
##   &amp; &amp; &amp; &amp; \\ 
## \hline \\[-1.8ex] 
## Number of respondents &amp; 107527 &amp; 156300 &amp; 581321 &amp; 274148 \\ 
## Number of surveys &amp; 101 &amp; 144 &amp; 521 &amp; 316 \\ 
## var(survey-level constants) &amp; 0.003 &amp; 0.007 &amp; 0.006 &amp; 0.012 \\ 
## sd(survey-level constants &amp; 0.053 &amp; 0.086 &amp; 0.076 &amp; 0.11 \\ 
## AIC &amp; -12179.665 &amp; -596.326 &amp; 75579.396 &amp; 56993.088 \\ 
## \hline 
## \hline \\[-1.8ex] 
## \textit{Note:}  &amp; \multicolumn{4}{r}{$^{*}$p$&lt;$0.05; $^{**}$p$&lt;$[0.**]; $^{***}$p$&lt;$[0.***]} \\ 
## \end{tabular} 
## \end{table}</code></pre>
</div>
<div id="base-generation-table-table-a2" class="section level4">
<h4>Base generation table-Table A2</h4>
<p>Table for model with generation control</p>
<div class="sourceCode" id="cb276"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb276-1"><a href="#cb276-1" tabindex="-1"></a><span class="co">#bring in the loaded models </span></span>
<span id="cb276-2"><a href="#cb276-2" tabindex="-1"></a>Americas_hyp1_generation <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Americas_hyp1_generation.rds&quot;</span>)</span>
<span id="cb276-3"><a href="#cb276-3" tabindex="-1"></a>CSES_hyp1_generation <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;CSES_hyp1_generation.rds&quot;</span>)</span>
<span id="cb276-4"><a href="#cb276-4" tabindex="-1"></a>Euro_hyp1_generation <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Euro_hyp1_generation.rds&quot;</span>)</span>
<span id="cb276-5"><a href="#cb276-5" tabindex="-1"></a>Latino_hyp1_generation <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Latino_hyp1_generation.rds&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb277"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb277-1"><a href="#cb277-1" tabindex="-1"></a>Americas_hyp1_generation.mod <span class="ot">&lt;-</span> broom.mixed<span class="sc">::</span><span class="fu">tidy</span>(Americas_hyp1_generation) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(effect <span class="sc">==</span> <span class="st">&quot;fixed&quot;</span>)</span>
<span id="cb277-2"><a href="#cb277-2" tabindex="-1"></a>CSES_hyp1_generation.mod <span class="ot">&lt;-</span> broom.mixed<span class="sc">::</span><span class="fu">tidy</span>(CSES_hyp1_generation) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(effect <span class="sc">==</span> <span class="st">&quot;fixed&quot;</span>)</span>
<span id="cb277-3"><a href="#cb277-3" tabindex="-1"></a></span>
<span id="cb277-4"><a href="#cb277-4" tabindex="-1"></a>Euro_hyp1_generation.mod <span class="ot">&lt;-</span> broom.mixed<span class="sc">::</span><span class="fu">tidy</span>(Euro_hyp1_generation) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(effect <span class="sc">==</span> <span class="st">&quot;fixed&quot;</span>)</span>
<span id="cb277-5"><a href="#cb277-5" tabindex="-1"></a>Latino_hyp1_generation.mod <span class="ot">&lt;-</span> broom.mixed<span class="sc">::</span><span class="fu">tidy</span>(Latino_hyp1_generation) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(effect <span class="sc">==</span> <span class="st">&quot;fixed&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb278"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb278-1"><a href="#cb278-1" tabindex="-1"></a><span class="co"># Number of unique surveys for that one random effect</span></span>
<span id="cb278-2"><a href="#cb278-2" tabindex="-1"></a>Americas_hyp1_generation_num_survey <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(<span class="fu">sapply</span>(lme4<span class="sc">::</span><span class="fu">ranef</span>(Americas_hyp1_generation),nrow)[<span class="dv">1</span>])</span>
<span id="cb278-3"><a href="#cb278-3" tabindex="-1"></a>CSES_hyp1_generation_num_survey <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(<span class="fu">sapply</span>(lme4<span class="sc">::</span><span class="fu">ranef</span>(CSES_hyp1_generation),nrow)[<span class="dv">1</span>])</span>
<span id="cb278-4"><a href="#cb278-4" tabindex="-1"></a>Euro_hyp1_generation_num_survey <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(<span class="fu">sapply</span>(lme4<span class="sc">::</span><span class="fu">ranef</span>(Euro_hyp1_generation),nrow)[<span class="dv">1</span>])</span>
<span id="cb278-5"><a href="#cb278-5" tabindex="-1"></a>Latino_hyp1_generation_num_survey <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(<span class="fu">sapply</span>(lme4<span class="sc">::</span><span class="fu">ranef</span>(Latino_hyp1_generation),nrow)[<span class="dv">1</span>])</span></code></pre></div>
<div class="sourceCode" id="cb279"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb279-1"><a href="#cb279-1" tabindex="-1"></a><span class="co"># variance for survey for base generation models</span></span>
<span id="cb279-2"><a href="#cb279-2" tabindex="-1"></a>var_Americas_hyp1_generation.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_random</span>(Americas_hyp1_generation)), <span class="dv">3</span>) </span>
<span id="cb279-3"><a href="#cb279-3" tabindex="-1"></a></span>
<span id="cb279-4"><a href="#cb279-4" tabindex="-1"></a>var_CSES_hyp1_generation.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_random</span>(CSES_hyp1_generation)), <span class="dv">3</span>) </span>
<span id="cb279-5"><a href="#cb279-5" tabindex="-1"></a></span>
<span id="cb279-6"><a href="#cb279-6" tabindex="-1"></a>var_Euro_hyp1_generation.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_random</span>(Euro_hyp1_generation)), <span class="dv">3</span>) </span>
<span id="cb279-7"><a href="#cb279-7" tabindex="-1"></a></span>
<span id="cb279-8"><a href="#cb279-8" tabindex="-1"></a>var_Latino_hyp1_generation.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_random</span>(Latino_hyp1_generation)), <span class="dv">3</span>)</span></code></pre></div>
<div class="sourceCode" id="cb280"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb280-1"><a href="#cb280-1" tabindex="-1"></a>sd_Americas_hyp1_generation.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(Americas_hyp1_generation)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev), <span class="dv">3</span>)</span>
<span id="cb280-2"><a href="#cb280-2" tabindex="-1"></a></span>
<span id="cb280-3"><a href="#cb280-3" tabindex="-1"></a>sd_CSES_hyp1_generation.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(CSES_hyp1_generation)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev), <span class="dv">3</span>)</span>
<span id="cb280-4"><a href="#cb280-4" tabindex="-1"></a>sd_Euro_hyp1_generation.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(Euro_hyp1_generation)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev), <span class="dv">3</span>)</span>
<span id="cb280-5"><a href="#cb280-5" tabindex="-1"></a>sd_Latino_hyp1_generation.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(Latino_hyp1_generation)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev), <span class="dv">3</span>)</span></code></pre></div>
<div class="sourceCode" id="cb281"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb281-1"><a href="#cb281-1" tabindex="-1"></a><span class="co"># AIC for models</span></span>
<span id="cb281-2"><a href="#cb281-2" tabindex="-1"></a>AIC_Americas_hyp1_generation.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">AIC</span>(<span class="fu">logLik</span>(Americas_hyp1_generation))), <span class="dv">3</span>)</span>
<span id="cb281-3"><a href="#cb281-3" tabindex="-1"></a>AIC_CSES_hyp1_generation.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">AIC</span>(<span class="fu">logLik</span>(CSES_hyp1_generation))), <span class="dv">3</span>)</span>
<span id="cb281-4"><a href="#cb281-4" tabindex="-1"></a>AIC_Euro_hyp1_generation.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">AIC</span>(<span class="fu">logLik</span>(Euro_hyp1_generation))), <span class="dv">3</span>)</span>
<span id="cb281-5"><a href="#cb281-5" tabindex="-1"></a>AIC_Latino_hyp1_generation.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">AIC</span>(<span class="fu">logLik</span>(Latino_hyp1_generation))), <span class="dv">3</span>)</span></code></pre></div>
<div class="sourceCode" id="cb282"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb282-1"><a href="#cb282-1" tabindex="-1"></a>hyp1_generation_stats<span class="ot">&lt;-</span> <span class="fu">tribble</span>(<span class="sc">~</span>stat, <span class="sc">~</span>Americas_hyp1_generation, <span class="sc">~</span>CSES_hyp1_generation, <span class="sc">~</span>Euro_hyp1_generation, <span class="sc">~</span>Latino_hyp1_generation,</span>
<span id="cb282-2"><a href="#cb282-2" tabindex="-1"></a><span class="st">&quot;Number of respondents&quot;</span>, <span class="fu">nobs</span>(Americas_hyp1_generation), <span class="fu">nobs</span>(CSES_hyp1_generation), <span class="fu">nobs</span>(Euro_hyp1_generation), <span class="fu">nobs</span>(Latino_hyp1_generation),      </span>
<span id="cb282-3"><a href="#cb282-3" tabindex="-1"></a><span class="st">&quot;Number of surveys&quot;</span>, Americas_hyp1_generation_num_survey, CSES_hyp1_generation_num_survey , Euro_hyp1_generation_num_survey, Latino_hyp1_generation_num_survey,</span>
<span id="cb282-4"><a href="#cb282-4" tabindex="-1"></a>        <span class="st">&quot;var(survey-level constants)&quot;</span>, var_Americas_hyp1_generation.mod,var_CSES_hyp1_generation.mod, var_Euro_hyp1_generation.mod, var_Latino_hyp1_generation.mod,</span>
<span id="cb282-5"><a href="#cb282-5" tabindex="-1"></a>        <span class="st">&quot;sd(survey-level constants&quot;</span>, sd_Americas_hyp1_generation.mod, sd_CSES_hyp1_generation.mod, sd_Euro_hyp1_generation.mod, sd_Latino_hyp1_generation.mod,</span>
<span id="cb282-6"><a href="#cb282-6" tabindex="-1"></a>        <span class="st">&quot;AIC&quot;</span>, AIC_Americas_hyp1_generation.mod,AIC_CSES_hyp1_generation.mod, AIC_Euro_hyp1_generation.mod, AIC_Latino_hyp1_generation.mod,</span>
<span id="cb282-7"><a href="#cb282-7" tabindex="-1"></a>        ) </span></code></pre></div>
<div class="sourceCode" id="cb283"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb283-1"><a href="#cb283-1" tabindex="-1"></a><span class="do">##shorter mod name otherwise there is a stargazer issue</span></span>
<span id="cb283-2"><a href="#cb283-2" tabindex="-1"></a></span>
<span id="cb283-3"><a href="#cb283-3" tabindex="-1"></a></span>
<span id="cb283-4"><a href="#cb283-4" tabindex="-1"></a>amer.gen <span class="ot">&lt;-</span> Americas_hyp1_generation</span>
<span id="cb283-5"><a href="#cb283-5" tabindex="-1"></a>cses.gen <span class="ot">&lt;-</span> CSES_hyp1_generation</span>
<span id="cb283-6"><a href="#cb283-6" tabindex="-1"></a>euro.gen <span class="ot">&lt;-</span> Euro_hyp1_generation</span>
<span id="cb283-7"><a href="#cb283-7" tabindex="-1"></a>lat.gen<span class="ot">&lt;-</span> Latino_hyp1_generation</span></code></pre></div>
<div class="sourceCode" id="cb284"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb284-1"><a href="#cb284-1" tabindex="-1"></a>stargazer<span class="sc">::</span><span class="fu">stargazer</span>(amer.gen,</span>
<span id="cb284-2"><a href="#cb284-2" tabindex="-1"></a>                     cses.gen, </span>
<span id="cb284-3"><a href="#cb284-3" tabindex="-1"></a>                     euro.gen, </span>
<span id="cb284-4"><a href="#cb284-4" tabindex="-1"></a>                     lat.gen, </span>
<span id="cb284-5"><a href="#cb284-5" tabindex="-1"></a>          <span class="at">type=</span><span class="st">&quot;latex&quot;</span>, </span>
<span id="cb284-6"><a href="#cb284-6" tabindex="-1"></a>          <span class="co"># ^  Notice M2 is called twice. I&#39;m going somewhere with this.</span></span>
<span id="cb284-7"><a href="#cb284-7" tabindex="-1"></a>          <span class="co"># Below: manually supply tidied coefficients and standard errors</span></span>
<span id="cb284-8"><a href="#cb284-8" tabindex="-1"></a>          <span class="at">coef =</span> <span class="fu">list</span>(Americas_hyp1_generation.mod<span class="sc">$</span>estimate ,CSES_hyp1_generation.mod<span class="sc">$</span>estimate, Euro_hyp1_generation.mod<span class="sc">$</span>estimate, Latino_hyp1_generation.mod<span class="sc">$</span>estimate),</span>
<span id="cb284-9"><a href="#cb284-9" tabindex="-1"></a>          <span class="at">se =</span> <span class="fu">list</span>(Americas_hyp1_generation.mod<span class="sc">$</span>std.error, CSES_hyp1_generation.mod<span class="sc">$</span>std.error, Euro_hyp1_generation.mod<span class="sc">$</span>std.error, Latino_hyp1_generation.mod<span class="sc">$</span>std.error),</span>
<span id="cb284-10"><a href="#cb284-10" tabindex="-1"></a>           <span class="at">star.char =</span> <span class="fu">c</span>( <span class="st">&quot;*&quot;</span>),</span>
<span id="cb284-11"><a href="#cb284-11" tabindex="-1"></a>          <span class="at">star.cutoffs =</span> <span class="fu">c</span>( .<span class="dv">05</span>),</span>
<span id="cb284-12"><a href="#cb284-12" tabindex="-1"></a>          <span class="co"># Omit model statistics by default...</span></span>
<span id="cb284-13"><a href="#cb284-13" tabindex="-1"></a>          <span class="at">omit.table.layout =</span> <span class="st">&quot;s&quot;</span>,</span>
<span id="cb284-14"><a href="#cb284-14" tabindex="-1"></a>          <span class="co"># ...but supply your own that you created (with random effects)</span></span>
<span id="cb284-15"><a href="#cb284-15" tabindex="-1"></a>          <span class="at">add.lines =</span> <span class="fu">lapply</span>(<span class="dv">1</span><span class="sc">:</span><span class="fu">nrow</span>(hyp1_generation_stats), <span class="cf">function</span>(i) <span class="fu">unlist</span>(hyp1_generation_stats[i, ])),</span>
<span id="cb284-16"><a href="#cb284-16" tabindex="-1"></a>        <span class="co">#  covariate.labels = c(&quot;Age&quot;,&quot;Female&quot;,&quot;Years of Education&quot;, &quot;Unemployed&quot;, &quot;Household Income (Deciles)&quot;, &quot;Ideology (L to R)&quot;),</span></span>
<span id="cb284-17"><a href="#cb284-17" tabindex="-1"></a>         <span class="co"># notes=&quot;&lt;small&gt;Data: ESS, Round 9 (United Kingdom)&lt;/small&gt;&quot;,</span></span>
<span id="cb284-18"><a href="#cb284-18" tabindex="-1"></a>        <span class="co">#  dep.var.labels=&quot;Pro-Immigration Sentiment&quot;,</span></span>
<span id="cb284-19"><a href="#cb284-19" tabindex="-1"></a>          <span class="at">model.names =</span> <span class="cn">FALSE</span>,</span>
<span id="cb284-20"><a href="#cb284-20" tabindex="-1"></a>          <span class="at">column.labels =</span> <span class="fu">c</span>(<span class="st">&quot;AmericasBarometer&quot;</span>,<span class="st">&quot;CSES&quot;</span>, <span class="st">&quot;Eurobarometer&quot;</span>, <span class="st">&quot;Latinobarometer&quot;</span>)</span>
<span id="cb284-21"><a href="#cb284-21" tabindex="-1"></a>          )</span></code></pre></div>
<pre><code>## 
## % Table created by stargazer v.5.2.3 by Marek Hlavac, Social Policy Institute. E-mail: marek.hlavac at gmail.com
## % Date and time: Tue, Jul 02, 2024 - 7:56:51 PM
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}}lcccc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
##  &amp; \multicolumn{4}{c}{\textit{Dependent variable:}} \\ 
## \cline{2-5} 
## \\[-1.8ex] &amp; \multicolumn{4}{c}{Satisfaction} \\ 
##  &amp; AmericasBarometer &amp; CSES &amp; Eurobarometer &amp; Latinobarometer \\ 
## \\[-1.8ex] &amp; (1) &amp; (2) &amp; (3) &amp; (4)\\ 
## \hline \\[-1.8ex] 
##  quota\_effectNon-Effective &amp; 0.029 &amp; 0.035 &amp; $-$0.015 &amp; $-$0.026 \\ 
##   &amp; (0.015) &amp; (0.032) &amp; (0.020) &amp; (0.018) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  quota\_effectEffective &amp; $-$0.016 &amp; $-$0.072$^{*}$ &amp; $-$0.084$^{*}$ &amp; $-$0.067$^{*}$ \\ 
##   &amp; (0.016) &amp; (0.022) &amp; (0.015) &amp; (0.018) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  FemaleWoman &amp; $-$0.007$^{*}$ &amp; $-$0.005$^{*}$ &amp; $-$0.005$^{*}$ &amp; $-$0.007$^{*}$ \\ 
##   &amp; (0.001) &amp; (0.001) &amp; (0.001) &amp; (0.001) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Ideology &amp; 0.019$^{*}$ &amp; 0.089$^{*}$ &amp; 0.088$^{*}$ &amp; 0.033$^{*}$ \\ 
##   &amp; (0.003) &amp; (0.003) &amp; (0.001) &amp; (0.002) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Education &amp; $-$0.057$^{*}$ &amp; 0.025$^{*}$ &amp;  &amp; $-$0.0002$^{*}$ \\ 
##   &amp; (0.003) &amp; (0.002) &amp;  &amp; (0.0001) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Income &amp; 0.004 &amp; 0.052$^{*}$ &amp;  &amp;  \\ 
##   &amp; (0.005) &amp; (0.002) &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Age\_education &amp;  &amp;  &amp; 0.046$^{*}$ &amp;  \\ 
##   &amp;  &amp;  &amp; (0.001) &amp;  \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Age &amp; $-$0.009 &amp; 0.027$^{*}$ &amp; 0.044$^{*}$ &amp; 0.0005$^{*}$ \\ 
##   &amp; (0.013) &amp; (0.010) &amp; (0.004) &amp; (0.0001) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  GenerationGen X &amp; $-$0.003 &amp; 0.009$^{*}$ &amp; 0.017$^{*}$ &amp; 0.007$^{*}$ \\ 
##   &amp; (0.003) &amp; (0.002) &amp; (0.001) &amp; (0.002) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  GenerationGen Z &amp; $-$0.004 &amp; 0.060$^{*}$ &amp; 0.054$^{*}$ &amp; 0.020$^{*}$ \\ 
##   &amp; (0.009) &amp; (0.014) &amp; (0.007) &amp; (0.007) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  GenerationMillennial &amp; $-$0.004 &amp; 0.017$^{*}$ &amp; 0.032$^{*}$ &amp; 0.011$^{*}$ \\ 
##   &amp; (0.005) &amp; (0.004) &amp; (0.002) &amp; (0.003) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  GenerationSilent/Greatest &amp; 0.013$^{*}$ &amp; 0.005 &amp; $-$0.005$^{*}$ &amp; $-$0.002 \\ 
##   &amp; (0.004) &amp; (0.003) &amp; (0.001) &amp; (0.003) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Polity\_score &amp; $-$0.019 &amp; $-$0.031 &amp; $-$0.003 &amp; 0.009 \\ 
##   &amp; (0.049) &amp; (0.074) &amp; (0.019) &amp; (0.005) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  GDP\_capita\_logged &amp; 0.056 &amp; 0.054 &amp; 0.162$^{*}$ &amp; 0.021 \\ 
##   &amp; (0.042) &amp; (0.069) &amp; (0.025) &amp; (0.016) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  women\_rep\_lag &amp; 0.037 &amp; 0.095$^{*}$ &amp; 0.002 &amp; 0.0002 \\ 
##   &amp; (0.040) &amp; (0.035) &amp; (0.020) &amp; (0.001) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Corruption &amp; $-$0.147$^{*}$ &amp; $-$0.198$^{*}$ &amp; $-$0.305$^{*}$ &amp; $-$0.260$^{*}$ \\ 
##   &amp; (0.034) &amp; (0.064) &amp; (0.039) &amp; (0.046) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Presidentialism &amp; 0.069 &amp; 0.016 &amp; $-$0.147 &amp; 0.252$^{*}$ \\ 
##   &amp; (0.035) &amp; (0.096) &amp; (0.109) &amp; (0.048) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Constant &amp; 0.576$^{*}$ &amp; 0.428$^{*}$ &amp; 0.414$^{*}$ &amp; 0.246 \\ 
##   &amp; (0.062) &amp; (0.094) &amp; (0.024) &amp; (0.174) \\ 
##   &amp; &amp; &amp; &amp; \\ 
## \hline \\[-1.8ex] 
## Number of respondents &amp; 107527 &amp; 156300 &amp; 581321 &amp; 274148 \\ 
## Number of surveys &amp; 101 &amp; 144 &amp; 521 &amp; 316 \\ 
## var(survey-level constants) &amp; 0.003 &amp; 0.007 &amp; 0.006 &amp; 0.012 \\ 
## sd(survey-level constants &amp; 0.053 &amp; 0.086 &amp; 0.077 &amp; 0.111 \\ 
## AIC &amp; -12147.692 &amp; -611.317 &amp; 75299.664 &amp; 57026.112 \\ 
## \hline 
## \hline \\[-1.8ex] 
## \textit{Note:}  &amp; \multicolumn{4}{r}{$^{*}$p$&lt;$0.05; $^{**}$p$&lt;$[0.**]; $^{***}$p$&lt;$[0.***]} \\ 
## \end{tabular} 
## \end{table}</code></pre>
</div>
<div id="gender-table-table-a3" class="section level4">
<h4>Gender table-Table A3</h4>
<div class="sourceCode" id="cb286"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb286-1"><a href="#cb286-1" tabindex="-1"></a><span class="co">#bring in the loaded models </span></span>
<span id="cb286-2"><a href="#cb286-2" tabindex="-1"></a>Americas_hyp2 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Americas_hyp2.rds&quot;</span>)</span>
<span id="cb286-3"><a href="#cb286-3" tabindex="-1"></a>CSES_hyp2 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;CSES_hyp2.rds&quot;</span>)</span>
<span id="cb286-4"><a href="#cb286-4" tabindex="-1"></a>Euro_hyp2 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Euro_hyp2.rds&quot;</span>)</span>
<span id="cb286-5"><a href="#cb286-5" tabindex="-1"></a>Latino_hyp2 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Latino_hyp2.rds&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb287"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb287-1"><a href="#cb287-1" tabindex="-1"></a>Americas_hyp2.mod <span class="ot">&lt;-</span> broom.mixed<span class="sc">::</span><span class="fu">tidy</span>(Americas_hyp2) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(effect <span class="sc">==</span> <span class="st">&quot;fixed&quot;</span>)</span>
<span id="cb287-2"><a href="#cb287-2" tabindex="-1"></a>CSES_hyp2.mod <span class="ot">&lt;-</span> broom.mixed<span class="sc">::</span><span class="fu">tidy</span>(CSES_hyp2) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(effect <span class="sc">==</span> <span class="st">&quot;fixed&quot;</span>)</span>
<span id="cb287-3"><a href="#cb287-3" tabindex="-1"></a></span>
<span id="cb287-4"><a href="#cb287-4" tabindex="-1"></a>Euro_hyp2.mod <span class="ot">&lt;-</span> broom.mixed<span class="sc">::</span><span class="fu">tidy</span>(Euro_hyp2) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(effect <span class="sc">==</span> <span class="st">&quot;fixed&quot;</span>)</span>
<span id="cb287-5"><a href="#cb287-5" tabindex="-1"></a>Latino_hyp2.mod <span class="ot">&lt;-</span> broom.mixed<span class="sc">::</span><span class="fu">tidy</span>(Latino_hyp2) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(effect <span class="sc">==</span> <span class="st">&quot;fixed&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb288"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb288-1"><a href="#cb288-1" tabindex="-1"></a><span class="co"># Number of unique surveys for that one random effect</span></span>
<span id="cb288-2"><a href="#cb288-2" tabindex="-1"></a>Americas_hyp2_num_survey <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(<span class="fu">sapply</span>(lme4<span class="sc">::</span><span class="fu">ranef</span>(Americas_hyp2),nrow)[<span class="dv">1</span>])</span>
<span id="cb288-3"><a href="#cb288-3" tabindex="-1"></a>CSES_hyp2_num_survey <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(<span class="fu">sapply</span>(lme4<span class="sc">::</span><span class="fu">ranef</span>(CSES_hyp2),nrow)[<span class="dv">1</span>])</span>
<span id="cb288-4"><a href="#cb288-4" tabindex="-1"></a>Euro_hyp2_num_survey <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(<span class="fu">sapply</span>(lme4<span class="sc">::</span><span class="fu">ranef</span>(Euro_hyp2),nrow)[<span class="dv">1</span>])</span>
<span id="cb288-5"><a href="#cb288-5" tabindex="-1"></a>Latino_hyp2_num_survey <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(<span class="fu">sapply</span>(lme4<span class="sc">::</span><span class="fu">ranef</span>(Latino_hyp2),nrow)[<span class="dv">1</span>])</span></code></pre></div>
<div class="sourceCode" id="cb289"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb289-1"><a href="#cb289-1" tabindex="-1"></a><span class="co"># variance for survey for gender models</span></span>
<span id="cb289-2"><a href="#cb289-2" tabindex="-1"></a>var_Americas_hyp2.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_random</span>(Americas_hyp2)), <span class="dv">3</span>) </span>
<span id="cb289-3"><a href="#cb289-3" tabindex="-1"></a></span>
<span id="cb289-4"><a href="#cb289-4" tabindex="-1"></a>var_CSES_hyp2.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_random</span>(CSES_hyp2)), <span class="dv">3</span>) </span>
<span id="cb289-5"><a href="#cb289-5" tabindex="-1"></a></span>
<span id="cb289-6"><a href="#cb289-6" tabindex="-1"></a>var_Euro_hyp2.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_random</span>(Euro_hyp2)), <span class="dv">3</span>) </span>
<span id="cb289-7"><a href="#cb289-7" tabindex="-1"></a></span>
<span id="cb289-8"><a href="#cb289-8" tabindex="-1"></a>var_Latino_hyp2.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_random</span>(Latino_hyp2)), <span class="dv">3</span>)</span></code></pre></div>
<div class="sourceCode" id="cb290"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb290-1"><a href="#cb290-1" tabindex="-1"></a><span class="co"># sd for survey for gender models</span></span>
<span id="cb290-2"><a href="#cb290-2" tabindex="-1"></a></span>
<span id="cb290-3"><a href="#cb290-3" tabindex="-1"></a>sd_Americas_hyp2.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(Americas_hyp2)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev), <span class="dv">3</span>)</span>
<span id="cb290-4"><a href="#cb290-4" tabindex="-1"></a></span>
<span id="cb290-5"><a href="#cb290-5" tabindex="-1"></a>sd_CSES_hyp2.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(CSES_hyp2)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev), <span class="dv">3</span>)</span>
<span id="cb290-6"><a href="#cb290-6" tabindex="-1"></a>sd_Euro_hyp2.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(Euro_hyp2)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev), <span class="dv">3</span>)</span>
<span id="cb290-7"><a href="#cb290-7" tabindex="-1"></a>sd_Latino_hyp2.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(Latino_hyp2)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev), <span class="dv">3</span>)</span></code></pre></div>
<div class="sourceCode" id="cb291"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb291-1"><a href="#cb291-1" tabindex="-1"></a><span class="co"># variance for slope for gender models</span></span>
<span id="cb291-2"><a href="#cb291-2" tabindex="-1"></a>var_slope_Americas_hyp2.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_slope</span>(Americas_hyp2)), <span class="dv">4</span>) </span>
<span id="cb291-3"><a href="#cb291-3" tabindex="-1"></a></span>
<span id="cb291-4"><a href="#cb291-4" tabindex="-1"></a>var_slope_CSES_hyp2.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_slope</span>(CSES_hyp2)), <span class="dv">4</span>) </span>
<span id="cb291-5"><a href="#cb291-5" tabindex="-1"></a></span>
<span id="cb291-6"><a href="#cb291-6" tabindex="-1"></a>var_slope_Euro_hyp2.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_slope</span>(Euro_hyp2)), <span class="dv">4</span>) </span>
<span id="cb291-7"><a href="#cb291-7" tabindex="-1"></a></span>
<span id="cb291-8"><a href="#cb291-8" tabindex="-1"></a>var_slope_Latino_hyp2.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_slope</span>(Latino_hyp2)), <span class="dv">4</span>)</span></code></pre></div>
<div class="sourceCode" id="cb292"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb292-1"><a href="#cb292-1" tabindex="-1"></a><span class="co"># sd for slope in gender models</span></span>
<span id="cb292-2"><a href="#cb292-2" tabindex="-1"></a>sd_slope_Americas_hyp2.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(Americas_hyp2)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev)[<span class="dv">2</span>], <span class="dv">3</span>)</span>
<span id="cb292-3"><a href="#cb292-3" tabindex="-1"></a></span>
<span id="cb292-4"><a href="#cb292-4" tabindex="-1"></a>sd_slope_CSES_hyp2.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(CSES_hyp2)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev)[<span class="dv">2</span>], <span class="dv">3</span>)</span>
<span id="cb292-5"><a href="#cb292-5" tabindex="-1"></a></span>
<span id="cb292-6"><a href="#cb292-6" tabindex="-1"></a>sd_slope_Euro_hyp2.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(Euro_hyp2)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev)[<span class="dv">2</span>], <span class="dv">3</span>)</span>
<span id="cb292-7"><a href="#cb292-7" tabindex="-1"></a></span>
<span id="cb292-8"><a href="#cb292-8" tabindex="-1"></a>sd_slope_Latino_hyp2.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(Latino_hyp2)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev)[<span class="dv">2</span>], <span class="dv">3</span>)</span></code></pre></div>
<div class="sourceCode" id="cb293"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb293-1"><a href="#cb293-1" tabindex="-1"></a><span class="co"># AIC for models</span></span>
<span id="cb293-2"><a href="#cb293-2" tabindex="-1"></a>AIC_Americas_hyp2.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">AIC</span>(<span class="fu">logLik</span>(Americas_hyp2))), <span class="dv">3</span>)</span>
<span id="cb293-3"><a href="#cb293-3" tabindex="-1"></a>AIC_CSES_hyp2.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">AIC</span>(<span class="fu">logLik</span>(CSES_hyp2))), <span class="dv">3</span>)</span>
<span id="cb293-4"><a href="#cb293-4" tabindex="-1"></a>AIC_Euro_hyp2.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">AIC</span>(<span class="fu">logLik</span>(Euro_hyp2))), <span class="dv">3</span>)</span>
<span id="cb293-5"><a href="#cb293-5" tabindex="-1"></a>AIC_Latino_hyp2.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">AIC</span>(<span class="fu">logLik</span>(Latino_hyp2))), <span class="dv">3</span>)</span></code></pre></div>
<div class="sourceCode" id="cb294"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb294-1"><a href="#cb294-1" tabindex="-1"></a>hyp_gender_stats<span class="ot">&lt;-</span> <span class="fu">tribble</span>(<span class="sc">~</span>stat, <span class="sc">~</span>Americas_hyp2, <span class="sc">~</span>CSES_hyp2, <span class="sc">~</span>Euro_hyp2, <span class="sc">~</span>Latino_hyp2,</span>
<span id="cb294-2"><a href="#cb294-2" tabindex="-1"></a><span class="st">&quot;Number of respondents&quot;</span>, <span class="fu">nobs</span>(Americas_hyp2), <span class="fu">nobs</span>(CSES_hyp2), <span class="fu">nobs</span>(Euro_hyp2), <span class="fu">nobs</span>(Latino_hyp2),      </span>
<span id="cb294-3"><a href="#cb294-3" tabindex="-1"></a><span class="st">&quot;Number of surveys&quot;</span>, Americas_hyp2_num_survey, CSES_hyp2_num_survey , Euro_hyp2_num_survey, Latino_hyp2_num_survey,</span>
<span id="cb294-4"><a href="#cb294-4" tabindex="-1"></a>        <span class="st">&quot;var(survey-level constants)&quot;</span>, var_Americas_hyp2.mod,var_CSES_hyp2.mod, var_Euro_hyp2.mod, var_Latino_hyp2.mod,</span>
<span id="cb294-5"><a href="#cb294-5" tabindex="-1"></a>        <span class="st">&quot;sd(survey-level constants)&quot;</span>, sd_Americas_hyp2.mod, sd_CSES_hyp2.mod, sd_Euro_hyp2.mod, sd_Latino_hyp2.mod,</span>
<span id="cb294-6"><a href="#cb294-6" tabindex="-1"></a>        <span class="st">&quot;var(Gender random coefficients)&quot;</span>, var_slope_Americas_hyp2.mod,var_slope_CSES_hyp2.mod, var_slope_Euro_hyp2.mod, var_slope_Latino_hyp2.mod,</span>
<span id="cb294-7"><a href="#cb294-7" tabindex="-1"></a>        <span class="st">&quot;sd(Gender random coefficients)&quot;</span>, sd_slope_Americas_hyp2.mod, sd_slope_CSES_hyp2.mod, sd_slope_Euro_hyp2.mod, sd_slope_Latino_hyp2.mod,</span>
<span id="cb294-8"><a href="#cb294-8" tabindex="-1"></a>        <span class="st">&quot;AIC&quot;</span>, AIC_Americas_hyp2.mod,AIC_CSES_hyp2.mod, AIC_Euro_hyp2.mod, AIC_Latino_hyp2.mod,</span>
<span id="cb294-9"><a href="#cb294-9" tabindex="-1"></a>        ) </span></code></pre></div>
<div class="sourceCode" id="cb295"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb295-1"><a href="#cb295-1" tabindex="-1"></a><span class="fu">stargazer</span>(Americas_hyp2, CSES_hyp2, Euro_hyp2, Latino_hyp2, </span>
<span id="cb295-2"><a href="#cb295-2" tabindex="-1"></a>          <span class="at">type=</span><span class="st">&quot;latex&quot;</span>, </span>
<span id="cb295-3"><a href="#cb295-3" tabindex="-1"></a>          <span class="co"># ^  Notice M2 is called twice. I&#39;m going somewhere with this.</span></span>
<span id="cb295-4"><a href="#cb295-4" tabindex="-1"></a>          <span class="co"># Below: manually supply tidied coefficients and standard errors</span></span>
<span id="cb295-5"><a href="#cb295-5" tabindex="-1"></a>          <span class="at">coef =</span> <span class="fu">list</span>(Americas_hyp2.mod<span class="sc">$</span>estimate ,CSES_hyp2.mod<span class="sc">$</span>estimate, Euro_hyp2.mod<span class="sc">$</span>estimate, Latino_hyp2.mod<span class="sc">$</span>estimate),</span>
<span id="cb295-6"><a href="#cb295-6" tabindex="-1"></a>          <span class="at">se =</span> <span class="fu">list</span>(Americas_hyp2.mod<span class="sc">$</span>std.error, CSES_hyp2.mod<span class="sc">$</span>std.error, Euro_hyp2.mod<span class="sc">$</span>std.error, Latino_hyp2.mod<span class="sc">$</span>std.error),</span>
<span id="cb295-7"><a href="#cb295-7" tabindex="-1"></a>           <span class="at">star.char =</span> <span class="fu">c</span>( <span class="st">&quot;*&quot;</span>),</span>
<span id="cb295-8"><a href="#cb295-8" tabindex="-1"></a>          <span class="at">star.cutoffs =</span> <span class="fu">c</span>( .<span class="dv">05</span>),</span>
<span id="cb295-9"><a href="#cb295-9" tabindex="-1"></a>          <span class="co"># Omit model statistics by default...</span></span>
<span id="cb295-10"><a href="#cb295-10" tabindex="-1"></a>          <span class="at">omit.table.layout =</span> <span class="st">&quot;s&quot;</span>,</span>
<span id="cb295-11"><a href="#cb295-11" tabindex="-1"></a>          <span class="co"># ...but supply your own that you created (with random effects)</span></span>
<span id="cb295-12"><a href="#cb295-12" tabindex="-1"></a>          <span class="at">add.lines =</span> <span class="fu">lapply</span>(<span class="dv">1</span><span class="sc">:</span><span class="fu">nrow</span>(hyp_gender_stats), <span class="cf">function</span>(i) <span class="fu">unlist</span>(hyp_gender_stats[i, ])),</span>
<span id="cb295-13"><a href="#cb295-13" tabindex="-1"></a>        <span class="co">#  covariate.labels = c(&quot;Age&quot;,&quot;Female&quot;,&quot;Years of Education&quot;, &quot;Unemployed&quot;, &quot;Household Income (Deciles)&quot;, &quot;Ideology (L to R)&quot;),</span></span>
<span id="cb295-14"><a href="#cb295-14" tabindex="-1"></a>         <span class="co"># notes=&quot;&lt;small&gt;Data: ESS, Round 9 (United Kingdom)&lt;/small&gt;&quot;,</span></span>
<span id="cb295-15"><a href="#cb295-15" tabindex="-1"></a>        <span class="co">#  dep.var.labels=&quot;Pro-Immigration Sentiment&quot;,</span></span>
<span id="cb295-16"><a href="#cb295-16" tabindex="-1"></a>          <span class="at">model.names =</span> <span class="cn">FALSE</span>,</span>
<span id="cb295-17"><a href="#cb295-17" tabindex="-1"></a>          <span class="at">column.labels =</span> <span class="fu">c</span>(<span class="st">&quot;AmericasBarometer&quot;</span>,<span class="st">&quot;CSES&quot;</span>, <span class="st">&quot;Eurobarometer&quot;</span>, <span class="st">&quot;Latinobarometer&quot;</span>)</span>
<span id="cb295-18"><a href="#cb295-18" tabindex="-1"></a>          )</span></code></pre></div>
<pre><code>## 
## % Table created by stargazer v.5.2.3 by Marek Hlavac, Social Policy Institute. E-mail: marek.hlavac at gmail.com
## % Date and time: Tue, Jul 02, 2024 - 7:56:58 PM
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}}lcccc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
##  &amp; \multicolumn{4}{c}{\textit{Dependent variable:}} \\ 
## \cline{2-5} 
## \\[-1.8ex] &amp; \multicolumn{4}{c}{Satisfaction} \\ 
##  &amp; AmericasBarometer &amp; CSES &amp; Eurobarometer &amp; Latinobarometer \\ 
## \\[-1.8ex] &amp; (1) &amp; (2) &amp; (3) &amp; (4)\\ 
## \hline \\[-1.8ex] 
##  quota\_effectNon-Effective &amp; 0.031$^{*}$ &amp; 0.042 &amp; $-$0.010 &amp; $-$0.023 \\ 
##   &amp; (0.016) &amp; (0.032) &amp; (0.021) &amp; (0.018) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  quota\_effectEffective &amp; $-$0.013 &amp; $-$0.073$^{*}$ &amp; $-$0.083$^{*}$ &amp; $-$0.068$^{*}$ \\ 
##   &amp; (0.017) &amp; (0.022) &amp; (0.015) &amp; (0.018) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  FemaleWoman &amp; $-$0.003 &amp; $-$0.004 &amp; $-$0.005$^{*}$ &amp; $-$0.005$^{*}$ \\ 
##   &amp; (0.003) &amp; (0.002) &amp; (0.001) &amp; (0.002) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Ideology &amp; 0.019$^{*}$ &amp; 0.089$^{*}$ &amp; 0.089$^{*}$ &amp; 0.033$^{*}$ \\ 
##   &amp; (0.003) &amp; (0.003) &amp; (0.001) &amp; (0.002) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Education &amp; $-$0.057$^{*}$ &amp; 0.024$^{*}$ &amp;  &amp; $-$0.0002$^{*}$ \\ 
##   &amp; (0.003) &amp; (0.002) &amp;  &amp; (0.0001) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Income &amp; 0.003 &amp; 0.049$^{*}$ &amp;  &amp;  \\ 
##   &amp; (0.005) &amp; (0.002) &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Age\_education &amp;  &amp;  &amp; 0.047$^{*}$ &amp;  \\ 
##   &amp;  &amp;  &amp; (0.001) &amp;  \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Age &amp; 0.010$^{*}$ &amp; 0.008$^{*}$ &amp; 0.004$^{*}$ &amp; 0.0002$^{*}$ \\ 
##   &amp; (0.004) &amp; (0.003) &amp; (0.002) &amp; (0.00003) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Polity\_score &amp; $-$0.006 &amp; $-$0.025 &amp; $-$0.005 &amp; 0.008 \\ 
##   &amp; (0.049) &amp; (0.073) &amp; (0.019) &amp; (0.005) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  GDP\_capita\_logged &amp; 0.053 &amp; 0.065 &amp; 0.174$^{*}$ &amp; 0.024 \\ 
##   &amp; (0.042) &amp; (0.068) &amp; (0.024) &amp; (0.016) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  women\_rep\_lag &amp; 0.035 &amp; 0.104$^{*}$ &amp; 0.006 &amp; 0.0004 \\ 
##   &amp; (0.039) &amp; (0.034) &amp; (0.020) &amp; (0.001) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Corruption &amp; $-$0.142$^{*}$ &amp; $-$0.186$^{*}$ &amp; $-$0.287$^{*}$ &amp; $-$0.248$^{*}$ \\ 
##   &amp; (0.034) &amp; (0.063) &amp; (0.039) &amp; (0.045) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Presidentialism &amp; 0.068 &amp; 0.026 &amp; $-$0.133 &amp; 0.247$^{*}$ \\ 
##   &amp; (0.035) &amp; (0.095) &amp; (0.107) &amp; (0.048) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  quota\_effectNon-Effective:FemaleWoman &amp; $-$0.006 &amp; $-$0.011 &amp; $-$0.007 &amp; $-$0.008$^{*}$ \\ 
##   &amp; (0.005) &amp; (0.008) &amp; (0.006) &amp; (0.003) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  quota\_effectEffective:FemaleWoman &amp; $-$0.006 &amp; 0.003 &amp; 0.007 &amp; $-$0.002 \\ 
##   &amp; (0.004) &amp; (0.006) &amp; (0.004) &amp; (0.003) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Constant &amp; 0.557$^{*}$ &amp; 0.420$^{*}$ &amp; 0.422$^{*}$ &amp; 0.232 \\ 
##   &amp; (0.061) &amp; (0.093) &amp; (0.024) &amp; (0.172) \\ 
##   &amp; &amp; &amp; &amp; \\ 
## \hline \\[-1.8ex] 
## Number of respondents &amp; 107527 &amp; 156300 &amp; 581321 &amp; 274148 \\ 
## Number of surveys &amp; 101 &amp; 144 &amp; 521 &amp; 316 \\ 
## var(survey-level constants) &amp; 0.003 &amp; 0.007 &amp; 0.006 &amp; 0.012 \\ 
## sd(survey-level constants) &amp; 0.054 &amp; 0.012 &amp; 0.088 &amp; 0.018 \\ 
## var(Gender random coefficients) &amp; 1e-04 &amp; 3e-04 &amp; 2e-04 &amp; 2e-04 \\ 
## sd(Gender random coefficients) &amp; 0.012 &amp; 0.018 &amp; 0.013 &amp; 0.012 \\ 
## AIC &amp; -12175.251 &amp; -644.157 &amp; 75487.997 &amp; 56982.604 \\ 
## \hline 
## \hline \\[-1.8ex] 
## \textit{Note:}  &amp; \multicolumn{4}{r}{$^{*}$p$&lt;$0.05; $^{**}$p$&lt;$[0.**]; $^{***}$p$&lt;$[0.***]} \\ 
## \end{tabular} 
## \end{table}</code></pre>
</div>
<div id="ideology-table-table-a4" class="section level4">
<h4>Ideology table-Table A4</h4>
<div class="sourceCode" id="cb297"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb297-1"><a href="#cb297-1" tabindex="-1"></a><span class="co">#bring in the loaded models </span></span>
<span id="cb297-2"><a href="#cb297-2" tabindex="-1"></a>Americas_hyp3a <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Americas_hyp3a.rds&quot;</span>)</span>
<span id="cb297-3"><a href="#cb297-3" tabindex="-1"></a>CSES_hyp3a <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;CSES_hyp3a.rds&quot;</span>)</span>
<span id="cb297-4"><a href="#cb297-4" tabindex="-1"></a>Euro_hyp3a <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Euro_hyp3a.rds&quot;</span>)</span>
<span id="cb297-5"><a href="#cb297-5" tabindex="-1"></a>Latino_hyp3a <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Latino_hyp3a.rds&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb298"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb298-1"><a href="#cb298-1" tabindex="-1"></a>Americas_hyp3a.mod <span class="ot">&lt;-</span> broom.mixed<span class="sc">::</span><span class="fu">tidy</span>(Americas_hyp3a) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(effect <span class="sc">==</span> <span class="st">&quot;fixed&quot;</span>)</span>
<span id="cb298-2"><a href="#cb298-2" tabindex="-1"></a>CSES_hyp3a.mod <span class="ot">&lt;-</span> broom.mixed<span class="sc">::</span><span class="fu">tidy</span>(CSES_hyp3a) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(effect <span class="sc">==</span> <span class="st">&quot;fixed&quot;</span>)</span>
<span id="cb298-3"><a href="#cb298-3" tabindex="-1"></a></span>
<span id="cb298-4"><a href="#cb298-4" tabindex="-1"></a>Euro_hyp3a.mod <span class="ot">&lt;-</span> broom.mixed<span class="sc">::</span><span class="fu">tidy</span>(Euro_hyp3a) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(effect <span class="sc">==</span> <span class="st">&quot;fixed&quot;</span>)</span>
<span id="cb298-5"><a href="#cb298-5" tabindex="-1"></a>Latino_hyp3a.mod <span class="ot">&lt;-</span> broom.mixed<span class="sc">::</span><span class="fu">tidy</span>(Latino_hyp3a) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(effect <span class="sc">==</span> <span class="st">&quot;fixed&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb299"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb299-1"><a href="#cb299-1" tabindex="-1"></a><span class="co"># Number of unique surveys for that one random effect</span></span>
<span id="cb299-2"><a href="#cb299-2" tabindex="-1"></a>Americas_hyp3a_num_survey <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(<span class="fu">sapply</span>(lme4<span class="sc">::</span><span class="fu">ranef</span>(Americas_hyp3a),nrow)[<span class="dv">1</span>])</span>
<span id="cb299-3"><a href="#cb299-3" tabindex="-1"></a>CSES_hyp3a_num_survey <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(<span class="fu">sapply</span>(lme4<span class="sc">::</span><span class="fu">ranef</span>(CSES_hyp3a),nrow)[<span class="dv">1</span>])</span>
<span id="cb299-4"><a href="#cb299-4" tabindex="-1"></a>Euro_hyp3a_num_survey <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(<span class="fu">sapply</span>(lme4<span class="sc">::</span><span class="fu">ranef</span>(Euro_hyp3a),nrow)[<span class="dv">1</span>])</span>
<span id="cb299-5"><a href="#cb299-5" tabindex="-1"></a>Latino_hyp3a_num_survey <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(<span class="fu">sapply</span>(lme4<span class="sc">::</span><span class="fu">ranef</span>(Latino_hyp3a),nrow)[<span class="dv">1</span>])</span></code></pre></div>
<div class="sourceCode" id="cb300"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb300-1"><a href="#cb300-1" tabindex="-1"></a><span class="co"># variance for survey for ideology models</span></span>
<span id="cb300-2"><a href="#cb300-2" tabindex="-1"></a>var_Americas_hyp3a.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_random</span>(Americas_hyp3a)), <span class="dv">3</span>) </span>
<span id="cb300-3"><a href="#cb300-3" tabindex="-1"></a></span>
<span id="cb300-4"><a href="#cb300-4" tabindex="-1"></a>var_CSES_hyp3a.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_random</span>(CSES_hyp3a)), <span class="dv">3</span>) </span>
<span id="cb300-5"><a href="#cb300-5" tabindex="-1"></a></span>
<span id="cb300-6"><a href="#cb300-6" tabindex="-1"></a>var_Euro_hyp3a.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_random</span>(Euro_hyp3a)), <span class="dv">3</span>) </span>
<span id="cb300-7"><a href="#cb300-7" tabindex="-1"></a></span>
<span id="cb300-8"><a href="#cb300-8" tabindex="-1"></a>var_Latino_hyp3a.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_random</span>(Latino_hyp3a)), <span class="dv">3</span>)</span></code></pre></div>
<div class="sourceCode" id="cb301"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb301-1"><a href="#cb301-1" tabindex="-1"></a><span class="co"># SD for survey in models</span></span>
<span id="cb301-2"><a href="#cb301-2" tabindex="-1"></a>sd_Americas_hyp3a.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(Americas_hyp3a)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev), <span class="dv">3</span>)</span>
<span id="cb301-3"><a href="#cb301-3" tabindex="-1"></a></span>
<span id="cb301-4"><a href="#cb301-4" tabindex="-1"></a>sd_CSES_hyp3a.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(CSES_hyp3a)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev), <span class="dv">3</span>)</span>
<span id="cb301-5"><a href="#cb301-5" tabindex="-1"></a></span>
<span id="cb301-6"><a href="#cb301-6" tabindex="-1"></a>sd_Euro_hyp3a.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(Euro_hyp3a)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev), <span class="dv">3</span>)</span>
<span id="cb301-7"><a href="#cb301-7" tabindex="-1"></a></span>
<span id="cb301-8"><a href="#cb301-8" tabindex="-1"></a>sd_Latino_hyp3a.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(Latino_hyp3a)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev), <span class="dv">3</span>)</span></code></pre></div>
<div class="sourceCode" id="cb302"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb302-1"><a href="#cb302-1" tabindex="-1"></a><span class="co"># variance for slope for ideology models</span></span>
<span id="cb302-2"><a href="#cb302-2" tabindex="-1"></a>var_slope_Americas_hyp3a.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_slope</span>(Americas_hyp3a)), <span class="dv">4</span>) </span>
<span id="cb302-3"><a href="#cb302-3" tabindex="-1"></a></span>
<span id="cb302-4"><a href="#cb302-4" tabindex="-1"></a>var_slope_CSES_hyp3a.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_slope</span>(CSES_hyp3a)), <span class="dv">4</span>) </span>
<span id="cb302-5"><a href="#cb302-5" tabindex="-1"></a></span>
<span id="cb302-6"><a href="#cb302-6" tabindex="-1"></a>var_slope_Euro_hyp3a.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_slope</span>(Euro_hyp3a)), <span class="dv">4</span>) </span>
<span id="cb302-7"><a href="#cb302-7" tabindex="-1"></a></span>
<span id="cb302-8"><a href="#cb302-8" tabindex="-1"></a>var_slope_Latino_hyp3a.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_slope</span>(Latino_hyp3a)), <span class="dv">4</span>)</span></code></pre></div>
<div class="sourceCode" id="cb303"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb303-1"><a href="#cb303-1" tabindex="-1"></a><span class="co"># SD for slope in ideology models</span></span>
<span id="cb303-2"><a href="#cb303-2" tabindex="-1"></a>sd_slope_Americas_hyp3a.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(Americas_hyp3a)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev)[<span class="dv">2</span>], <span class="dv">3</span>)</span>
<span id="cb303-3"><a href="#cb303-3" tabindex="-1"></a></span>
<span id="cb303-4"><a href="#cb303-4" tabindex="-1"></a>sd_slope_CSES_hyp3a.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(CSES_hyp3a)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev)[<span class="dv">2</span>], <span class="dv">3</span>)</span>
<span id="cb303-5"><a href="#cb303-5" tabindex="-1"></a></span>
<span id="cb303-6"><a href="#cb303-6" tabindex="-1"></a>sd_slope_Euro_hyp3a.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(Euro_hyp3a)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev)[<span class="dv">2</span>], <span class="dv">3</span>)</span>
<span id="cb303-7"><a href="#cb303-7" tabindex="-1"></a></span>
<span id="cb303-8"><a href="#cb303-8" tabindex="-1"></a>sd_slope_Latino_hyp3a.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(Latino_hyp3a)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev)[<span class="dv">2</span>], <span class="dv">3</span>)</span></code></pre></div>
<div class="sourceCode" id="cb304"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb304-1"><a href="#cb304-1" tabindex="-1"></a><span class="co"># AIC for models</span></span>
<span id="cb304-2"><a href="#cb304-2" tabindex="-1"></a>AIC_Americas_hyp3a.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">AIC</span>(<span class="fu">logLik</span>(Americas_hyp3a))), <span class="dv">3</span>)</span>
<span id="cb304-3"><a href="#cb304-3" tabindex="-1"></a>AIC_CSES_hyp3a.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">AIC</span>(<span class="fu">logLik</span>(CSES_hyp3a))), <span class="dv">3</span>)</span>
<span id="cb304-4"><a href="#cb304-4" tabindex="-1"></a>AIC_Euro_hyp3a.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">AIC</span>(<span class="fu">logLik</span>(Euro_hyp3a))), <span class="dv">3</span>)</span>
<span id="cb304-5"><a href="#cb304-5" tabindex="-1"></a>AIC_Latino_hyp3a.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">AIC</span>(<span class="fu">logLik</span>(Latino_hyp3a))), <span class="dv">3</span>)</span></code></pre></div>
<div class="sourceCode" id="cb305"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb305-1"><a href="#cb305-1" tabindex="-1"></a>hyp_ideology_stats<span class="ot">&lt;-</span> <span class="fu">tribble</span>(<span class="sc">~</span>stat, <span class="sc">~</span>Americas_hyp3a, <span class="sc">~</span>CSES_hyp3a, <span class="sc">~</span>Euro_hyp3a, <span class="sc">~</span>Latino_hyp3a,</span>
<span id="cb305-2"><a href="#cb305-2" tabindex="-1"></a><span class="st">&quot;Number of respondents&quot;</span>, <span class="fu">nobs</span>(Americas_hyp3a), <span class="fu">nobs</span>(CSES_hyp3a), <span class="fu">nobs</span>(Euro_hyp3a), <span class="fu">nobs</span>(Latino_hyp3a),      </span>
<span id="cb305-3"><a href="#cb305-3" tabindex="-1"></a><span class="st">&quot;Number of surveys&quot;</span>, Americas_hyp3a_num_survey, CSES_hyp3a_num_survey , Euro_hyp3a_num_survey, Latino_hyp3a_num_survey,</span>
<span id="cb305-4"><a href="#cb305-4" tabindex="-1"></a>        <span class="st">&quot;var(survey-level constants)&quot;</span>, var_Americas_hyp3a.mod,var_CSES_hyp3a.mod, var_Euro_hyp3a.mod, var_Latino_hyp3a.mod,</span>
<span id="cb305-5"><a href="#cb305-5" tabindex="-1"></a>        <span class="st">&quot;sd(survey-level constants)&quot;</span>, sd_Americas_hyp3a.mod, sd_CSES_hyp3a.mod, sd_Euro_hyp3a.mod, sd_Latino_hyp3a.mod,</span>
<span id="cb305-6"><a href="#cb305-6" tabindex="-1"></a>        <span class="st">&quot;var(Gender random coefficients)&quot;</span>, var_slope_Americas_hyp3a.mod,var_slope_CSES_hyp3a.mod, var_slope_Euro_hyp3a.mod, var_slope_Latino_hyp3a.mod,</span>
<span id="cb305-7"><a href="#cb305-7" tabindex="-1"></a>        <span class="st">&quot;sd(Gender random coefficients)&quot;</span>, sd_slope_Americas_hyp3a.mod, sd_slope_CSES_hyp3a.mod, sd_slope_Euro_hyp3a.mod, sd_slope_Latino_hyp3a.mod,</span>
<span id="cb305-8"><a href="#cb305-8" tabindex="-1"></a>        <span class="st">&quot;AIC&quot;</span>, AIC_Americas_hyp3a.mod,AIC_CSES_hyp3a.mod, AIC_Euro_hyp3a.mod, AIC_Latino_hyp3a.mod,</span>
<span id="cb305-9"><a href="#cb305-9" tabindex="-1"></a>        ) </span></code></pre></div>
<div class="sourceCode" id="cb306"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb306-1"><a href="#cb306-1" tabindex="-1"></a><span class="fu">stargazer</span>(Americas_hyp3a, CSES_hyp3a, Euro_hyp3a, Latino_hyp3a, </span>
<span id="cb306-2"><a href="#cb306-2" tabindex="-1"></a>          <span class="at">type=</span><span class="st">&quot;latex&quot;</span>, </span>
<span id="cb306-3"><a href="#cb306-3" tabindex="-1"></a>          <span class="at">coef =</span> <span class="fu">list</span>(Americas_hyp3a.mod<span class="sc">$</span>estimate ,CSES_hyp3a.mod<span class="sc">$</span>estimate, Euro_hyp3a.mod<span class="sc">$</span>estimate, Latino_hyp3a.mod<span class="sc">$</span>estimate),</span>
<span id="cb306-4"><a href="#cb306-4" tabindex="-1"></a>          <span class="at">se =</span> <span class="fu">list</span>(Americas_hyp3a.mod<span class="sc">$</span>std.error, CSES_hyp3a.mod<span class="sc">$</span>std.error, Euro_hyp3a.mod<span class="sc">$</span>std.error, Latino_hyp3a.mod<span class="sc">$</span>std.error),</span>
<span id="cb306-5"><a href="#cb306-5" tabindex="-1"></a>           <span class="at">star.char =</span> <span class="fu">c</span>( <span class="st">&quot;*&quot;</span>),</span>
<span id="cb306-6"><a href="#cb306-6" tabindex="-1"></a>          <span class="at">star.cutoffs =</span> <span class="fu">c</span>( .<span class="dv">05</span>),</span>
<span id="cb306-7"><a href="#cb306-7" tabindex="-1"></a>          <span class="co"># Omit model statistics by default...</span></span>
<span id="cb306-8"><a href="#cb306-8" tabindex="-1"></a>          <span class="at">omit.table.layout =</span> <span class="st">&quot;s&quot;</span>,</span>
<span id="cb306-9"><a href="#cb306-9" tabindex="-1"></a>          <span class="co"># ...but supply your own that you created (with random effects)</span></span>
<span id="cb306-10"><a href="#cb306-10" tabindex="-1"></a>          <span class="at">add.lines =</span> <span class="fu">lapply</span>(<span class="dv">1</span><span class="sc">:</span><span class="fu">nrow</span>(hyp_ideology_stats), <span class="cf">function</span>(i) <span class="fu">unlist</span>(hyp_ideology_stats[i, ])),</span>
<span id="cb306-11"><a href="#cb306-11" tabindex="-1"></a></span>
<span id="cb306-12"><a href="#cb306-12" tabindex="-1"></a>          <span class="at">model.names =</span> <span class="cn">FALSE</span>,</span>
<span id="cb306-13"><a href="#cb306-13" tabindex="-1"></a>          <span class="at">column.labels =</span> <span class="fu">c</span>(<span class="st">&quot;AmericasBarometer&quot;</span>,<span class="st">&quot;CSES&quot;</span>, <span class="st">&quot;Eurobarometer&quot;</span>, <span class="st">&quot;Latinobarometer&quot;</span>)</span>
<span id="cb306-14"><a href="#cb306-14" tabindex="-1"></a>          )</span></code></pre></div>
<pre><code>## 
## % Table created by stargazer v.5.2.3 by Marek Hlavac, Social Policy Institute. E-mail: marek.hlavac at gmail.com
## % Date and time: Tue, Jul 02, 2024 - 7:57:06 PM
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}}lcccc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
##  &amp; \multicolumn{4}{c}{\textit{Dependent variable:}} \\ 
## \cline{2-5} 
## \\[-1.8ex] &amp; \multicolumn{4}{c}{Satisfaction} \\ 
##  &amp; AmericasBarometer &amp; CSES &amp; Eurobarometer &amp; Latinobarometer \\ 
## \\[-1.8ex] &amp; (1) &amp; (2) &amp; (3) &amp; (4)\\ 
## \hline \\[-1.8ex] 
##  quota\_effectNon-Effective &amp; $-$0.021 &amp; 0.088$^{*}$ &amp; $-$0.010 &amp; $-$0.051$^{*}$ \\ 
##   &amp; (0.023) &amp; (0.042) &amp; (0.027) &amp; (0.022) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  quota\_effectEffective &amp; $-$0.044$^{*}$ &amp; $-$0.069$^{*}$ &amp; $-$0.034 &amp; $-$0.079$^{*}$ \\ 
##   &amp; (0.022) &amp; (0.030) &amp; (0.020) &amp; (0.021) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Ideology &amp; $-$0.011 &amp; 0.101$^{*}$ &amp; 0.094$^{*}$ &amp; 0.022 \\ 
##   &amp; (0.019) &amp; (0.014) &amp; (0.007) &amp; (0.012) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  FemaleWoman &amp; $-$0.007$^{*}$ &amp; $-$0.006$^{*}$ &amp; $-$0.005$^{*}$ &amp; $-$0.007$^{*}$ \\ 
##   &amp; (0.001) &amp; (0.001) &amp; (0.001) &amp; (0.001) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Education &amp; $-$0.052$^{*}$ &amp; 0.024$^{*}$ &amp;  &amp; $-$0.0002$^{*}$ \\ 
##   &amp; (0.003) &amp; (0.002) &amp;  &amp; (0.0001) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Income &amp; 0.002 &amp; 0.049$^{*}$ &amp;  &amp;  \\ 
##   &amp; (0.004) &amp; (0.002) &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Age\_education &amp;  &amp;  &amp; 0.046$^{*}$ &amp;  \\ 
##   &amp;  &amp;  &amp; (0.001) &amp;  \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Age &amp; 0.007 &amp; 0.011$^{*}$ &amp; 0.002 &amp; 0.0001$^{*}$ \\ 
##   &amp; (0.004) &amp; (0.003) &amp; (0.002) &amp; (0.00003) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Polity\_score &amp; $-$0.007 &amp; $-$0.027 &amp; $-$0.006 &amp; 0.011$^{*}$ \\ 
##   &amp; (0.047) &amp; (0.074) &amp; (0.019) &amp; (0.005) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  GDP\_capita\_logged &amp; 0.050 &amp; 0.070 &amp; 0.177$^{*}$ &amp; 0.022 \\ 
##   &amp; (0.040) &amp; (0.069) &amp; (0.024) &amp; (0.016) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  women\_rep\_lag &amp; 0.036 &amp; 0.092$^{*}$ &amp; 0.012 &amp; 0.0002 \\ 
##   &amp; (0.037) &amp; (0.035) &amp; (0.019) &amp; (0.001) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Corruption &amp; $-$0.128$^{*}$ &amp; $-$0.184$^{*}$ &amp; $-$0.279$^{*}$ &amp; $-$0.242$^{*}$ \\ 
##   &amp; (0.032) &amp; (0.064) &amp; (0.038) &amp; (0.046) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Presidentialism &amp; 0.047 &amp; 0.014 &amp; $-$0.152 &amp; 0.242$^{*}$ \\ 
##   &amp; (0.033) &amp; (0.096) &amp; (0.106) &amp; (0.048) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  quota\_effectNon-Effective:Ideology &amp; 0.093$^{*}$ &amp; $-$0.092 &amp; 0.001 &amp; 0.045$^{*}$ \\ 
##   &amp; (0.031) &amp; (0.048) &amp; (0.038) &amp; (0.022) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  quota\_effectEffective:Ideology &amp; 0.052 &amp; $-$0.013 &amp; $-$0.097$^{*}$ &amp; 0.024 \\ 
##   &amp; (0.028) &amp; (0.035) &amp; (0.029) &amp; (0.019) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Constant &amp; 0.573$^{*}$ &amp; 0.419$^{*}$ &amp; 0.419$^{*}$ &amp; 0.236 \\ 
##   &amp; (0.059) &amp; (0.094) &amp; (0.024) &amp; (0.173) \\ 
##   &amp; &amp; &amp; &amp; \\ 
## \hline \\[-1.8ex] 
## Number of respondents &amp; 107527 &amp; 156300 &amp; 581321 &amp; 274148 \\ 
## Number of surveys &amp; 101 &amp; 144 &amp; 521 &amp; 316 \\ 
## var(survey-level constants) &amp; 0.004 &amp; 0.009 &amp; 0.007 &amp; 0.014 \\ 
## sd(survey-level constants) &amp; 0.085 &amp; 0.12 &amp; 0.119 &amp; 0.142 \\ 
## var(Gender random coefficients) &amp; 0.0143 &amp; 0.0203 &amp; 0.0209 &amp; 0.0212 \\ 
## sd(Gender random coefficients) &amp; 0.12 &amp; 0.142 &amp; 0.145 &amp; 0.146 \\ 
## AIC &amp; -14451.784 &amp; -3111.969 &amp; 65666.892 &amp; 51381.586 \\ 
## \hline 
## \hline \\[-1.8ex] 
## \textit{Note:}  &amp; \multicolumn{4}{r}{$^{*}$p$&lt;$0.05; $^{**}$p$&lt;$[0.**]; $^{***}$p$&lt;$[0.***]} \\ 
## \end{tabular} 
## \end{table}</code></pre>
</div>
<div id="quota-support-table-table-a5" class="section level4">
<h4>Quota support table-Table A5</h4>
<div class="sourceCode" id="cb308"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb308-1"><a href="#cb308-1" tabindex="-1"></a><span class="co">#bring in the loaded models </span></span>
<span id="cb308-2"><a href="#cb308-2" tabindex="-1"></a>Americas_hyp3b <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Americas_hyp3b.rds&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb309"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb309-1"><a href="#cb309-1" tabindex="-1"></a>Americas_hyp3b.mod <span class="ot">&lt;-</span> broom.mixed<span class="sc">::</span><span class="fu">tidy</span>(Americas_hyp3b) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(effect <span class="sc">==</span> <span class="st">&quot;fixed&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb310"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb310-1"><a href="#cb310-1" tabindex="-1"></a><span class="co"># Number of unique surveys for that one random effect</span></span>
<span id="cb310-2"><a href="#cb310-2" tabindex="-1"></a>Americas_hyp3b_num_survey <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(<span class="fu">sapply</span>(lme4<span class="sc">::</span><span class="fu">ranef</span>(Americas_hyp3b),nrow)[<span class="dv">1</span>])</span></code></pre></div>
<div class="sourceCode" id="cb311"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb311-1"><a href="#cb311-1" tabindex="-1"></a><span class="co"># variance for survey for support model</span></span>
<span id="cb311-2"><a href="#cb311-2" tabindex="-1"></a>var_Americas_hyp3b.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_random</span>(Americas_hyp3b)), <span class="dv">3</span>) </span></code></pre></div>
<div class="sourceCode" id="cb312"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb312-1"><a href="#cb312-1" tabindex="-1"></a><span class="co"># SD for survey in model</span></span>
<span id="cb312-2"><a href="#cb312-2" tabindex="-1"></a>sd_Americas_hyp3b.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(Americas_hyp3b)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev), <span class="dv">3</span>)</span></code></pre></div>
<div class="sourceCode" id="cb313"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb313-1"><a href="#cb313-1" tabindex="-1"></a><span class="co"># AIC for model</span></span>
<span id="cb313-2"><a href="#cb313-2" tabindex="-1"></a>AIC_Americas_hyp3b.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">AIC</span>(<span class="fu">logLik</span>(Americas_hyp3b))), <span class="dv">3</span>)</span></code></pre></div>
<div class="sourceCode" id="cb314"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb314-1"><a href="#cb314-1" tabindex="-1"></a>hyp3b_stats<span class="ot">&lt;-</span> <span class="fu">tribble</span>(<span class="sc">~</span>stat, <span class="sc">~</span>Americas_hyp3b,</span>
<span id="cb314-2"><a href="#cb314-2" tabindex="-1"></a><span class="st">&quot;Number of respondents&quot;</span>, <span class="fu">nobs</span>(Americas_hyp3b),</span>
<span id="cb314-3"><a href="#cb314-3" tabindex="-1"></a><span class="st">&quot;Number of surveys&quot;</span>, Americas_hyp3b_num_survey, </span>
<span id="cb314-4"><a href="#cb314-4" tabindex="-1"></a>        <span class="st">&quot;var(survey-level constants)&quot;</span>, var_Americas_hyp3b.mod,</span>
<span id="cb314-5"><a href="#cb314-5" tabindex="-1"></a>        <span class="st">&quot;sd(survey-level constants)&quot;</span>, sd_Americas_hyp3b.mod,</span>
<span id="cb314-6"><a href="#cb314-6" tabindex="-1"></a>        <span class="st">&quot;AIC&quot;</span>, AIC_Americas_hyp3b.mod,</span>
<span id="cb314-7"><a href="#cb314-7" tabindex="-1"></a>        ) </span></code></pre></div>
<div class="sourceCode" id="cb315"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb315-1"><a href="#cb315-1" tabindex="-1"></a><span class="fu">stargazer</span>(Americas_hyp3b, </span>
<span id="cb315-2"><a href="#cb315-2" tabindex="-1"></a>          <span class="at">type=</span><span class="st">&quot;latex&quot;</span>, </span>
<span id="cb315-3"><a href="#cb315-3" tabindex="-1"></a>          <span class="at">coef =</span> <span class="fu">list</span>(Americas_hyp3b.mod<span class="sc">$</span>estimate),</span>
<span id="cb315-4"><a href="#cb315-4" tabindex="-1"></a>          <span class="at">se =</span> <span class="fu">list</span>(Americas_hyp3b.mod<span class="sc">$</span>std.error),</span>
<span id="cb315-5"><a href="#cb315-5" tabindex="-1"></a>           <span class="at">star.char =</span> <span class="fu">c</span>( <span class="st">&quot;*&quot;</span>),</span>
<span id="cb315-6"><a href="#cb315-6" tabindex="-1"></a>          <span class="at">star.cutoffs =</span> <span class="fu">c</span>( .<span class="dv">05</span>),</span>
<span id="cb315-7"><a href="#cb315-7" tabindex="-1"></a>          <span class="co"># Omit model statistics by default...</span></span>
<span id="cb315-8"><a href="#cb315-8" tabindex="-1"></a>          <span class="at">omit.table.layout =</span> <span class="st">&quot;s&quot;</span>,</span>
<span id="cb315-9"><a href="#cb315-9" tabindex="-1"></a>          <span class="co"># ...but supply your own that you created (with random effects)</span></span>
<span id="cb315-10"><a href="#cb315-10" tabindex="-1"></a>          <span class="at">add.lines =</span> <span class="fu">lapply</span>(<span class="dv">1</span><span class="sc">:</span><span class="fu">nrow</span>(hyp3b_stats), <span class="cf">function</span>(i) <span class="fu">unlist</span>(hyp3b_stats[i, ])),</span>
<span id="cb315-11"><a href="#cb315-11" tabindex="-1"></a>          <span class="at">model.names =</span> <span class="cn">FALSE</span>,</span>
<span id="cb315-12"><a href="#cb315-12" tabindex="-1"></a>          <span class="at">column.labels =</span> <span class="fu">c</span>(<span class="st">&quot;AmericasBarometer&quot;</span>)</span>
<span id="cb315-13"><a href="#cb315-13" tabindex="-1"></a>          )</span></code></pre></div>
<pre><code>## 
## % Table created by stargazer v.5.2.3 by Marek Hlavac, Social Policy Institute. E-mail: marek.hlavac at gmail.com
## % Date and time: Tue, Jul 02, 2024 - 7:57:07 PM
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}}lc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
##  &amp; \multicolumn{1}{c}{\textit{Dependent variable:}} \\ 
## \cline{2-2} 
## \\[-1.8ex] &amp; Satisfaction \\ 
##  &amp; AmericasBarometer \\ 
## \hline \\[-1.8ex] 
##  quota\_effectNon-Effective &amp; 0.032 \\ 
##   &amp; (0.037) \\ 
##   &amp; \\ 
##  quota\_effectEffective &amp; $-$0.012 \\ 
##   &amp; (0.031) \\ 
##   &amp; \\ 
##  Quota\_support &amp; $-$0.007 \\ 
##   &amp; (0.011) \\ 
##   &amp; \\ 
##  FemaleWoman &amp; $-$0.007 \\ 
##   &amp; (0.004) \\ 
##   &amp; \\ 
##  Ideology &amp; 0.036$^{*}$ \\ 
##   &amp; (0.007) \\ 
##   &amp; \\ 
##  Education &amp; $-$0.058$^{*}$ \\ 
##   &amp; (0.010) \\ 
##   &amp; \\ 
##  Income &amp; $-$0.010 \\ 
##   &amp; (0.009) \\ 
##   &amp; \\ 
##  Age &amp; 0.010 \\ 
##   &amp; (0.011) \\ 
##   &amp; \\ 
##  Polity\_score &amp; $-$0.025 \\ 
##   &amp; (0.087) \\ 
##   &amp; \\ 
##  GDP\_capita\_logged &amp; 0.009 \\ 
##   &amp; (0.104) \\ 
##   &amp; \\ 
##  women\_rep\_lag &amp; $-$0.013 \\ 
##   &amp; (0.084) \\ 
##   &amp; \\ 
##  Corruption &amp; $-$0.155$^{*}$ \\ 
##   &amp; (0.071) \\ 
##   &amp; \\ 
##  Presidentialism &amp; 0.138 \\ 
##   &amp; (0.074) \\ 
##   &amp; \\ 
##  quota\_effectNon-Effective:Quota\_support &amp; $-$0.023 \\ 
##   &amp; (0.016) \\ 
##   &amp; \\ 
##  quota\_effectEffective:Quota\_support &amp; 0.027 \\ 
##   &amp; (0.015) \\ 
##   &amp; \\ 
##  Constant &amp; 0.599$^{*}$ \\ 
##   &amp; (0.132) \\ 
##   &amp; \\ 
## \hline \\[-1.8ex] 
## Number of respondents &amp; 13648 \\ 
## Number of surveys &amp; 23 \\ 
## var(survey-level constants) &amp; 0.002 \\ 
## sd(survey-level constants) &amp; 0.049 \\ 
## AIC &amp; -2324.816 \\ 
## \hline 
## \hline \\[-1.8ex] 
## \textit{Note:}  &amp; \multicolumn{1}{r}{$^{*}$p$&lt;$0.05; $^{**}$p$&lt;$[0.**]; $^{***}$p$&lt;$[0.***]} \\ 
## \end{tabular} 
## \end{table}</code></pre>
</div>
<div id="corruption-table-table-a6" class="section level4">
<h4>Corruption table-Table A6</h4>
<div class="sourceCode" id="cb317"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb317-1"><a href="#cb317-1" tabindex="-1"></a><span class="co">#bring in the loaded models </span></span>
<span id="cb317-2"><a href="#cb317-2" tabindex="-1"></a>Americas_hyp4 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Americas_hyp4.rds&quot;</span>)</span>
<span id="cb317-3"><a href="#cb317-3" tabindex="-1"></a>CSES_hyp4 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;CSES_hyp4.rds&quot;</span>)</span>
<span id="cb317-4"><a href="#cb317-4" tabindex="-1"></a>Euro_hyp4 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Euro_hyp4.rds&quot;</span>)</span>
<span id="cb317-5"><a href="#cb317-5" tabindex="-1"></a>Latino_hyp4 <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;Latino_hyp4.rds&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb318"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb318-1"><a href="#cb318-1" tabindex="-1"></a><span class="fu">library</span>(broom.mixed)</span>
<span id="cb318-2"><a href="#cb318-2" tabindex="-1"></a>Americas_hyp4.mod <span class="ot">&lt;-</span> broom.mixed<span class="sc">::</span><span class="fu">tidy</span>(Americas_hyp4) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(effect <span class="sc">==</span> <span class="st">&quot;fixed&quot;</span>)</span>
<span id="cb318-3"><a href="#cb318-3" tabindex="-1"></a>CSES_hyp4.mod <span class="ot">&lt;-</span> broom.mixed<span class="sc">::</span><span class="fu">tidy</span>(CSES_hyp4) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(effect <span class="sc">==</span> <span class="st">&quot;fixed&quot;</span>)</span>
<span id="cb318-4"><a href="#cb318-4" tabindex="-1"></a></span>
<span id="cb318-5"><a href="#cb318-5" tabindex="-1"></a>Euro_hyp4.mod <span class="ot">&lt;-</span> broom.mixed<span class="sc">::</span><span class="fu">tidy</span>(Euro_hyp4) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(effect <span class="sc">==</span> <span class="st">&quot;fixed&quot;</span>)</span>
<span id="cb318-6"><a href="#cb318-6" tabindex="-1"></a>Latino_hyp4.mod <span class="ot">&lt;-</span> broom.mixed<span class="sc">::</span><span class="fu">tidy</span>(Latino_hyp4) <span class="sc">%&gt;%</span> <span class="fu">filter</span>(effect <span class="sc">==</span> <span class="st">&quot;fixed&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb319"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb319-1"><a href="#cb319-1" tabindex="-1"></a><span class="co"># Number of unique surveys for that one random effect</span></span>
<span id="cb319-2"><a href="#cb319-2" tabindex="-1"></a>Americas_hyp4_num_survey <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(<span class="fu">sapply</span>(lme4<span class="sc">::</span><span class="fu">ranef</span>(Americas_hyp4),nrow)[<span class="dv">1</span>])</span>
<span id="cb319-3"><a href="#cb319-3" tabindex="-1"></a></span>
<span id="cb319-4"><a href="#cb319-4" tabindex="-1"></a>CSES_hyp4_num_survey <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(<span class="fu">sapply</span>(lme4<span class="sc">::</span><span class="fu">ranef</span>(CSES_hyp4),nrow)[<span class="dv">1</span>])</span>
<span id="cb319-5"><a href="#cb319-5" tabindex="-1"></a></span>
<span id="cb319-6"><a href="#cb319-6" tabindex="-1"></a>Euro_hyp4_num_survey <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(<span class="fu">sapply</span>(lme4<span class="sc">::</span><span class="fu">ranef</span>(Euro_hyp4),nrow)[<span class="dv">1</span>])</span>
<span id="cb319-7"><a href="#cb319-7" tabindex="-1"></a></span>
<span id="cb319-8"><a href="#cb319-8" tabindex="-1"></a>Latino_hyp4_num_survey <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(<span class="fu">sapply</span>(lme4<span class="sc">::</span><span class="fu">ranef</span>(Latino_hyp4),nrow)[<span class="dv">1</span>])</span></code></pre></div>
<div class="sourceCode" id="cb320"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb320-1"><a href="#cb320-1" tabindex="-1"></a><span class="co"># variance for survey for corruption models</span></span>
<span id="cb320-2"><a href="#cb320-2" tabindex="-1"></a>var_Americas_hyp4.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_random</span>(Americas_hyp4)), <span class="dv">3</span>) </span>
<span id="cb320-3"><a href="#cb320-3" tabindex="-1"></a></span>
<span id="cb320-4"><a href="#cb320-4" tabindex="-1"></a>var_CSES_hyp4.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_random</span>(CSES_hyp4)), <span class="dv">3</span>) </span>
<span id="cb320-5"><a href="#cb320-5" tabindex="-1"></a></span>
<span id="cb320-6"><a href="#cb320-6" tabindex="-1"></a>var_Euro_hyp4.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_random</span>(Euro_hyp4)), <span class="dv">3</span>) </span>
<span id="cb320-7"><a href="#cb320-7" tabindex="-1"></a></span>
<span id="cb320-8"><a href="#cb320-8" tabindex="-1"></a>var_Latino_hyp4.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">get_variance_random</span>(Latino_hyp4)), <span class="dv">3</span>)</span></code></pre></div>
<div class="sourceCode" id="cb321"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb321-1"><a href="#cb321-1" tabindex="-1"></a><span class="co"># SD for survey random effect</span></span>
<span id="cb321-2"><a href="#cb321-2" tabindex="-1"></a>sd_Americas_hyp4.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(Americas_hyp4)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev), <span class="dv">3</span>)</span>
<span id="cb321-3"><a href="#cb321-3" tabindex="-1"></a></span>
<span id="cb321-4"><a href="#cb321-4" tabindex="-1"></a>sd_CSES_hyp4.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(CSES_hyp4)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev), <span class="dv">3</span>)</span>
<span id="cb321-5"><a href="#cb321-5" tabindex="-1"></a></span>
<span id="cb321-6"><a href="#cb321-6" tabindex="-1"></a>sd_Euro_hyp4.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(Euro_hyp4)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev), <span class="dv">3</span>)</span>
<span id="cb321-7"><a href="#cb321-7" tabindex="-1"></a></span>
<span id="cb321-8"><a href="#cb321-8" tabindex="-1"></a>sd_Latino_hyp4.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">attributes</span>(lme4<span class="sc">::</span><span class="fu">VarCorr</span>(Latino_hyp4)<span class="sc">$</span><span class="st">&quot;Country_year&quot;</span>)<span class="sc">$</span>stddev), <span class="dv">3</span>)</span></code></pre></div>
<div class="sourceCode" id="cb322"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb322-1"><a href="#cb322-1" tabindex="-1"></a><span class="co"># AIC for models</span></span>
<span id="cb322-2"><a href="#cb322-2" tabindex="-1"></a>AIC_Americas_hyp4.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">AIC</span>(<span class="fu">logLik</span>(Americas_hyp4))), <span class="dv">3</span>)</span>
<span id="cb322-3"><a href="#cb322-3" tabindex="-1"></a></span>
<span id="cb322-4"><a href="#cb322-4" tabindex="-1"></a>AIC_CSES_hyp4.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">AIC</span>(<span class="fu">logLik</span>(CSES_hyp4))), <span class="dv">3</span>)</span>
<span id="cb322-5"><a href="#cb322-5" tabindex="-1"></a></span>
<span id="cb322-6"><a href="#cb322-6" tabindex="-1"></a>AIC_Euro_hyp4.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">AIC</span>(<span class="fu">logLik</span>(Euro_hyp4))), <span class="dv">3</span>)</span>
<span id="cb322-7"><a href="#cb322-7" tabindex="-1"></a></span>
<span id="cb322-8"><a href="#cb322-8" tabindex="-1"></a>AIC_Latino_hyp4.mod <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">as.numeric</span>(<span class="fu">AIC</span>(<span class="fu">logLik</span>(Latino_hyp4))), <span class="dv">3</span>)</span></code></pre></div>
<div class="sourceCode" id="cb323"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb323-1"><a href="#cb323-1" tabindex="-1"></a>hyp4_stats<span class="ot">&lt;-</span> <span class="fu">tribble</span>(<span class="sc">~</span>stat, <span class="sc">~</span>Americas_hyp4, <span class="sc">~</span>CSES_hyp4, <span class="sc">~</span>Euro_hyp4, <span class="sc">~</span>Latino_hyp4,</span>
<span id="cb323-2"><a href="#cb323-2" tabindex="-1"></a><span class="st">&quot;Number of respondents&quot;</span>, <span class="fu">nobs</span>(Americas_hyp4), <span class="fu">nobs</span>(CSES_hyp4), <span class="fu">nobs</span>(Euro_hyp4), <span class="fu">nobs</span>(Latino_hyp4),      </span>
<span id="cb323-3"><a href="#cb323-3" tabindex="-1"></a><span class="st">&quot;Number of surveys&quot;</span>, Americas_hyp4_num_survey, CSES_hyp4_num_survey , Euro_hyp4_num_survey, Latino_hyp4_num_survey,</span>
<span id="cb323-4"><a href="#cb323-4" tabindex="-1"></a>        <span class="st">&quot;var(survey-level constants)&quot;</span>, var_Americas_hyp4.mod,var_CSES_hyp4.mod, var_Euro_hyp4.mod, var_Latino_hyp4.mod,</span>
<span id="cb323-5"><a href="#cb323-5" tabindex="-1"></a>        <span class="st">&quot;sd(survey-level constants&quot;</span>, sd_Americas_hyp4.mod, sd_CSES_hyp4.mod, sd_Euro_hyp4.mod, sd_Latino_hyp4.mod,</span>
<span id="cb323-6"><a href="#cb323-6" tabindex="-1"></a>        <span class="st">&quot;AIC&quot;</span>, AIC_Americas_hyp4.mod,AIC_CSES_hyp4.mod, AIC_Euro_hyp4.mod, AIC_Latino_hyp4.mod,</span>
<span id="cb323-7"><a href="#cb323-7" tabindex="-1"></a>        ) </span></code></pre></div>
<div class="sourceCode" id="cb324"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb324-1"><a href="#cb324-1" tabindex="-1"></a><span class="fu">stargazer</span>(Americas_hyp4, CSES_hyp4, Euro_hyp4, Latino_hyp4, </span>
<span id="cb324-2"><a href="#cb324-2" tabindex="-1"></a>          <span class="at">type=</span><span class="st">&quot;latex&quot;</span>, </span>
<span id="cb324-3"><a href="#cb324-3" tabindex="-1"></a>          <span class="at">coef =</span> <span class="fu">list</span>(Americas_hyp4.mod<span class="sc">$</span>estimate ,CSES_hyp4.mod<span class="sc">$</span>estimate, Euro_hyp4.mod<span class="sc">$</span>estimate, Latino_hyp4.mod<span class="sc">$</span>estimate),</span>
<span id="cb324-4"><a href="#cb324-4" tabindex="-1"></a>          <span class="at">se =</span> <span class="fu">list</span>(Americas_hyp4.mod<span class="sc">$</span>std.error, CSES_hyp4.mod<span class="sc">$</span>std.error, Euro_hyp4.mod<span class="sc">$</span>std.error, Latino_hyp4.mod<span class="sc">$</span>std.error),</span>
<span id="cb324-5"><a href="#cb324-5" tabindex="-1"></a>           <span class="at">star.char =</span> <span class="fu">c</span>( <span class="st">&quot;*&quot;</span>),</span>
<span id="cb324-6"><a href="#cb324-6" tabindex="-1"></a>          <span class="at">star.cutoffs =</span> <span class="fu">c</span>( .<span class="dv">05</span>),</span>
<span id="cb324-7"><a href="#cb324-7" tabindex="-1"></a>          <span class="co"># Omit model statistics by default...</span></span>
<span id="cb324-8"><a href="#cb324-8" tabindex="-1"></a>          <span class="at">omit.table.layout =</span> <span class="st">&quot;s&quot;</span>,</span>
<span id="cb324-9"><a href="#cb324-9" tabindex="-1"></a>          <span class="co"># ...but supply your own that you created (with random effects)</span></span>
<span id="cb324-10"><a href="#cb324-10" tabindex="-1"></a>          <span class="at">add.lines =</span> <span class="fu">lapply</span>(<span class="dv">1</span><span class="sc">:</span><span class="fu">nrow</span>(hyp4_stats), <span class="cf">function</span>(i) <span class="fu">unlist</span>(hyp4_stats[i, ])),</span>
<span id="cb324-11"><a href="#cb324-11" tabindex="-1"></a>          <span class="at">model.names =</span> <span class="cn">FALSE</span>,</span>
<span id="cb324-12"><a href="#cb324-12" tabindex="-1"></a>          <span class="at">column.labels =</span> <span class="fu">c</span>(<span class="st">&quot;AmericasBarometer&quot;</span>,<span class="st">&quot;CSES&quot;</span>, <span class="st">&quot;Eurobarometer&quot;</span>, <span class="st">&quot;Latinobarometer&quot;</span>)</span>
<span id="cb324-13"><a href="#cb324-13" tabindex="-1"></a>          )</span></code></pre></div>
<pre><code>## 
## % Table created by stargazer v.5.2.3 by Marek Hlavac, Social Policy Institute. E-mail: marek.hlavac at gmail.com
## % Date and time: Tue, Jul 02, 2024 - 7:57:13 PM
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}}lcccc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
##  &amp; \multicolumn{4}{c}{\textit{Dependent variable:}} \\ 
## \cline{2-5} 
## \\[-1.8ex] &amp; \multicolumn{4}{c}{Satisfaction} \\ 
##  &amp; AmericasBarometer &amp; CSES &amp; Eurobarometer &amp; Latinobarometer \\ 
## \\[-1.8ex] &amp; (1) &amp; (2) &amp; (3) &amp; (4)\\ 
## \hline \\[-1.8ex] 
##  quota\_effectNon-Effective &amp; 0.068 &amp; $-$0.040 &amp; $-$0.109$^{*}$ &amp; $-$0.052 \\ 
##   &amp; (0.064) &amp; (0.054) &amp; (0.026) &amp; (0.073) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  quota\_effectEffective &amp; 0.060 &amp; $-$0.108$^{*}$ &amp; $-$0.066$^{*}$ &amp; 0.087$^{*}$ \\ 
##   &amp; (0.034) &amp; (0.035) &amp; (0.020) &amp; (0.043) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Corruption &amp; $-$0.118$^{*}$ &amp; $-$0.265$^{*}$ &amp; $-$0.278$^{*}$ &amp; $-$0.210$^{*}$ \\ 
##   &amp; (0.035) &amp; (0.072) &amp; (0.038) &amp; (0.046) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  FemaleWoman &amp; $-$0.007$^{*}$ &amp; $-$0.005$^{*}$ &amp; $-$0.005$^{*}$ &amp; $-$0.007$^{*}$ \\ 
##   &amp; (0.001) &amp; (0.001) &amp; (0.001) &amp; (0.001) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Ideology &amp; 0.019$^{*}$ &amp; 0.089$^{*}$ &amp; 0.089$^{*}$ &amp; 0.033$^{*}$ \\ 
##   &amp; (0.003) &amp; (0.003) &amp; (0.001) &amp; (0.002) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Education &amp; $-$0.057$^{*}$ &amp; 0.024$^{*}$ &amp;  &amp; $-$0.0002$^{*}$ \\ 
##   &amp; (0.003) &amp; (0.002) &amp;  &amp; (0.0001) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Income &amp; 0.003 &amp; 0.049$^{*}$ &amp;  &amp;  \\ 
##   &amp; (0.005) &amp; (0.002) &amp;  &amp;  \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Age\_education &amp;  &amp;  &amp; 0.047$^{*}$ &amp;  \\ 
##   &amp;  &amp;  &amp; (0.001) &amp;  \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Age &amp; 0.010$^{*}$ &amp; 0.008$^{*}$ &amp; 0.004$^{*}$ &amp; 0.0002$^{*}$ \\ 
##   &amp; (0.004) &amp; (0.003) &amp; (0.002) &amp; (0.00003) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Polity\_score &amp; $-$0.003 &amp; $-$0.041 &amp; $-$0.029 &amp; 0.010$^{*}$ \\ 
##   &amp; (0.048) &amp; (0.073) &amp; (0.019) &amp; (0.005) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  GDP\_capita\_logged &amp; 0.063 &amp; 0.064 &amp; 0.207$^{*}$ &amp; 0.038$^{*}$ \\ 
##   &amp; (0.043) &amp; (0.068) &amp; (0.024) &amp; (0.016) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  women\_rep\_lag &amp; 0.013 &amp; 0.080$^{*}$ &amp; $-$0.018 &amp; $-$0.001 \\ 
##   &amp; (0.041) &amp; (0.036) &amp; (0.020) &amp; (0.001) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Presidentialism &amp; 0.080$^{*}$ &amp; 0.073 &amp; $-$0.327$^{*}$ &amp; 0.278$^{*}$ \\ 
##   &amp; (0.035) &amp; (0.099) &amp; (0.109) &amp; (0.048) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  quota\_effectNon-Effective:Corruption &amp; $-$0.060 &amp; 0.187 &amp; 0.389$^{*}$ &amp; 0.025 \\ 
##   &amp; (0.082) &amp; (0.105) &amp; (0.071) &amp; (0.103) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  quota\_effectEffective:Corruption &amp; $-$0.121$^{*}$ &amp; 0.118 &amp; $-$0.130 &amp; $-$0.247$^{*}$ \\ 
##   &amp; (0.048) &amp; (0.079) &amp; (0.125) &amp; (0.063) \\ 
##   &amp; &amp; &amp; &amp; \\ 
##  Constant &amp; 0.539$^{*}$ &amp; 0.456$^{*}$ &amp; 0.457$^{*}$ &amp; 0.078 \\ 
##   &amp; (0.062) &amp; (0.093) &amp; (0.024) &amp; (0.175) \\ 
##   &amp; &amp; &amp; &amp; \\ 
## \hline \\[-1.8ex] 
## Number of respondents &amp; 107527 &amp; 156300 &amp; 581321 &amp; 274148 \\ 
## Number of surveys &amp; 101 &amp; 144 &amp; 521 &amp; 316 \\ 
## var(survey-level constants) &amp; 0.003 &amp; 0.007 &amp; 0.005 &amp; 0.012 \\ 
## sd(survey-level constants &amp; 0.052 &amp; 0.085 &amp; 0.074 &amp; 0.108 \\ 
## AIC &amp; -12174.776 &amp; -590.87 &amp; 75557.955 &amp; 56988.137 \\ 
## \hline 
## \hline \\[-1.8ex] 
## \textit{Note:}  &amp; \multicolumn{4}{r}{$^{*}$p$&lt;$0.05; $^{**}$p$&lt;$[0.**]; $^{***}$p$&lt;$[0.***]} \\ 
## \end{tabular} 
## \end{table}</code></pre>
</div>
</div>
</div>
</div>
<div id="study-2" class="section level1">
<h1>Study 2</h1>
<p>Code to estimate the generalized synthetic control model, as well as
interpret, visualize, and diagnose model and results. The four treated
countries are Greece, Mexico, Portugal, and Spain.</p>
<div id="load-needed-packages-and-functions-1" class="section level2">
<h2>Load Needed packages and functions</h2>
<div class="sourceCode" id="cb326"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb326-1"><a href="#cb326-1" tabindex="-1"></a><span class="fu">library</span>(dplyr)</span>
<span id="cb326-2"><a href="#cb326-2" tabindex="-1"></a><span class="fu">library</span>(Synth)</span></code></pre></div>
<pre><code>## ##
## ## Synth Package: Implements Synthetic Control Methods.</code></pre>
<pre><code>## ## See https://web.stanford.edu/~jhain/synthpage.html for additional information.</code></pre>
<div class="sourceCode" id="cb329"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb329-1"><a href="#cb329-1" tabindex="-1"></a><span class="fu">library</span>(gsynth)</span></code></pre></div>
<pre><code>## ## Syntax has been updated since v.1.2.0.</code></pre>
<pre><code>## ## Comments and suggestions -&gt; yiqingxu@stanford.edu.</code></pre>
<div class="sourceCode" id="cb332"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb332-1"><a href="#cb332-1" tabindex="-1"></a><span class="fu">library</span>(panelView)</span></code></pre></div>
<pre><code>## Warning: package &#39;panelView&#39; was built under R version 4.3.3</code></pre>
<pre><code>## ## See bit.ly/panelview4r for more info.
## ## Report bugs -&gt; yiqingxu@stanford.edu.</code></pre>
<div class="sourceCode" id="cb335"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb335-1"><a href="#cb335-1" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb335-2"><a href="#cb335-2" tabindex="-1"></a><span class="fu">library</span>(stargazer)</span></code></pre></div>
</div>
<div id="load-analysis-data" class="section level2">
<h2>Load Analysis Data</h2>
<div class="sourceCode" id="cb336"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb336-1"><a href="#cb336-1" tabindex="-1"></a>Synth_analysis <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Analysis Data/Synth_analysis.rds&quot;</span>)</span></code></pre></div>
</div>
<div id="model-estimation-1" class="section level2">
<h2>Model Estimation</h2>
<p>Estimate the generalized synthetic control model. The three treated
countries are Greece, Portugal, and Spain.</p>
<div class="sourceCode" id="cb337"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb337-1"><a href="#cb337-1" tabindex="-1"></a>   synth_mod <span class="ot">&lt;-</span> <span class="fu">gsynth</span>(Satisfaction_mean <span class="sc">~</span> Quota  <span class="sc">+</span> Satisfaction_diff,</span>
<span id="cb337-2"><a href="#cb337-2" tabindex="-1"></a>                  <span class="at">data =</span> Synth_analysis, <span class="at">index =</span> <span class="fu">c</span>(<span class="st">&quot;Country&quot;</span>,<span class="st">&quot;Year&quot;</span>), <span class="at">force =</span> <span class="st">&quot;two-way&quot;</span>, <span class="at">CV =</span> <span class="cn">TRUE</span>, <span class="at">r =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">3</span>),</span>
<span id="cb337-3"><a href="#cb337-3" tabindex="-1"></a>                  <span class="at">se =</span> <span class="cn">TRUE</span>, <span class="at">estimator =</span> <span class="st">&quot;ife&quot;</span>, <span class="at">inference =</span> <span class="st">&quot;parametric&quot;</span>, <span class="at">EM =</span> <span class="cn">TRUE</span>,</span>
<span id="cb337-4"><a href="#cb337-4" tabindex="-1"></a>                  <span class="at">nboots =</span> <span class="dv">1000</span>, <span class="at">parallel =</span> <span class="cn">FALSE</span>,</span>
<span id="cb337-5"><a href="#cb337-5" tabindex="-1"></a>                 <span class="at">min.T0 =</span> <span class="dv">5</span>) <span class="co">#minimum pre-treatment</span></span></code></pre></div>
<pre><code>## Cross-validating ... 
##  r = 0; sigma2 = 0.00534; IC = -4.43911; PC = 0.00467; MSPE = 0.01164*
##  r = 1; sigma2 = 0.00368; IC = -4.05316; PC = 0.00467; MSPE = 0.02288
##  r = 2; sigma2 = 0.00238; IC = -3.75459; PC = 0.00396; MSPE = 0.01243
##  r = 3; sigma2 = 0.00158; IC = -3.44877; PC = 0.00327; MSPE = 0.00732
## 
##  r* = 3
## 
## 
Bootstrapping ...
## ..........</code></pre>
<div class="sourceCode" id="cb339"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb339-1"><a href="#cb339-1" tabindex="-1"></a><span class="fu">print</span>(synth_mod)</span></code></pre></div>
<pre><code>## Call:
## gsynth.formula(formula = Satisfaction_mean ~ Quota + Satisfaction_diff, 
##     data = Synth_analysis, index = c(&quot;Country&quot;, &quot;Year&quot;), force = &quot;two-way&quot;, 
##     r = c(0, 3), CV = TRUE, EM = TRUE, estimator = &quot;ife&quot;, se = TRUE, 
##     nboots = 1000, inference = &quot;parametric&quot;, parallel = FALSE, 
##     min.T0 = 5)
## 
## Average Treatment Effect on the Treated:
##         Estimate    S.E. CI.lower CI.upper   p.value
## ATT.avg  -0.1046 0.02441  -0.1525 -0.05678 1.822e-05
## 
##    ~ by Period (including Pre-treatment Periods):
##            ATT     S.E.  CI.lower   CI.upper   p.value n.Treated
## -26  1.458e-02 0.005119  0.004548  2.461e-02 4.393e-03         0
## -25  3.637e-02 0.015261  0.006462  6.628e-02 1.715e-02         0
## -24  2.067e-02 0.012606 -0.004035  4.538e-02 1.010e-01         0
## -23  2.220e-02 0.012511 -0.002326  4.672e-02 7.606e-02         0
## -22 -3.763e-03 0.009234 -0.021860  1.433e-02 6.836e-01         0
## -21 -5.276e-03 0.008772 -0.022468  1.192e-02 5.475e-01         0
## -20 -2.297e-02 0.008112 -0.038868 -7.070e-03 4.632e-03         0
## -19 -1.597e-02 0.008156 -0.031957  1.373e-05 5.020e-02         0
## -18  1.797e-02 0.011364 -0.004298  4.025e-02 1.137e-01         0
## -17  3.182e-02 0.008016  0.016105  4.753e-02 7.214e-05         0
## -16  2.544e-02 0.007029  0.011665  3.922e-02 2.951e-04         0
## -15 -6.847e-05 0.013882 -0.027277  2.714e-02 9.961e-01         0
## -14  3.429e-03 0.007189 -0.010661  1.752e-02 6.334e-01         0
## -13 -3.077e-02 0.011020 -0.052372 -9.173e-03 5.233e-03         0
## -12 -1.443e-02 0.012697 -0.039316  1.046e-02 2.558e-01         0
## -11 -2.465e-02 0.010971 -0.046157 -3.152e-03 2.462e-02         0
## -10 -3.444e-02 0.015235 -0.064297 -4.575e-03 2.381e-02         0
## -9  -1.665e-02 0.012988 -0.042104  8.806e-03 1.999e-01         0
## -8  -3.030e-03 0.015099 -0.032624  2.656e-02 8.409e-01         0
## -7   1.432e-02 0.012734 -0.010635  3.928e-02 2.607e-01         0
## -6   4.791e-03 0.012848 -0.020390  2.997e-02 7.092e-01         0
## -5   1.019e-03 0.012608 -0.023693  2.573e-02 9.356e-01         0
## -4  -9.447e-03 0.011352 -0.031696  1.280e-02 4.053e-01         0
## -3   8.980e-04 0.012448 -0.023500  2.530e-02 9.425e-01         0
## -2   1.774e-02 0.009754 -0.001373  3.686e-02 6.889e-02         0
## -1   2.917e-02 0.010749  0.008102  5.024e-02 6.653e-03         0
## 0   -5.912e-02 0.016219 -0.090905 -2.733e-02 2.676e-04         0
## 1   -4.065e-02 0.023768 -0.087232  5.936e-03 8.723e-02         4
## 2   -1.210e-02 0.030649 -0.072171  4.797e-02 6.930e-01         4
## 3   -5.500e-02 0.029154 -0.112141  2.140e-03 5.922e-02         4
## 4   -8.912e-02 0.035139 -0.157991 -2.025e-02 1.121e-02         3
## 5   -1.453e-01 0.035404 -0.214731 -7.595e-02 4.039e-05         3
## 6   -2.415e-01 0.030628 -0.301572 -1.815e-01 3.109e-15         3
## 7   -1.391e-01 0.058807 -0.254313 -2.379e-02 1.805e-02         1
## 8   -1.897e-01 0.072245 -0.331287 -4.809e-02 8.648e-03         1
## 9   -1.875e-01 0.070211 -0.325110 -4.989e-02 7.574e-03         1
## 10         NaN       NA       NaN         NA       NaN         0
## 11  -2.405e-01 0.062957 -0.363883 -1.171e-01 1.335e-04         1
## 12         NaN       NA       NaN         NA       NaN         0
## 
## Coefficients for the Covariates:
##                     beta    S.E. CI.lower CI.upper p.value
## Satisfaction_diff 0.5082 0.02093   0.4672   0.5492       0</code></pre>
<div class="sourceCode" id="cb341"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb341-1"><a href="#cb341-1" tabindex="-1"></a><span class="fu">saveRDS</span>(synth_mod, <span class="st">&quot;synth_mod.rds&quot;</span>)</span></code></pre></div>
<div id="model-main-results" class="section level3">
<h3>Model main results</h3>
<div class="sourceCode" id="cb342"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb342-1"><a href="#cb342-1" tabindex="-1"></a>est.att <span class="ot">&lt;-</span>   synth_mod<span class="sc">$</span>est.att</span>
<span id="cb342-2"><a href="#cb342-2" tabindex="-1"></a></span>
<span id="cb342-3"><a href="#cb342-3" tabindex="-1"></a><span class="co"># Average effect on the treated (ATT)</span></span>
<span id="cb342-4"><a href="#cb342-4" tabindex="-1"></a>    synth_mod<span class="sc">$</span>est.avg</span></code></pre></div>
<pre><code>##           Estimate      S.E.   CI.lower    CI.upper      p.value
## ATT.avg -0.1046293 0.0244138 -0.1524795 -0.05677918 1.821937e-05</code></pre>
<div class="sourceCode" id="cb344"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb344-1"><a href="#cb344-1" tabindex="-1"></a><span class="co"># Estimated beta     </span></span>
<span id="cb344-2"><a href="#cb344-2" tabindex="-1"></a>    synth_mod<span class="sc">$</span>est.beta</span></code></pre></div>
<pre><code>##                       beta       S.E.  CI.lower  CI.upper p.value
## Satisfaction_diff 0.508208 0.02092823 0.4671894 0.5492266       0</code></pre>
</div>
</div>
<div id="figures-main-text" class="section level2">
<h2>Figures-Main text</h2>
<div id="average-figures" class="section level3">
<h3>Average figures</h3>
<div class="sourceCode" id="cb346"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb346-1"><a href="#cb346-1" tabindex="-1"></a>p1 <span class="ot">&lt;-</span>    <span class="fu">plot</span>(synth_mod, <span class="at">type =</span> <span class="st">&quot;counterfactual&quot;</span>, <span class="at">raw =</span> <span class="st">&quot;all&quot;</span>, <span class="at">legendOff =</span> <span class="cn">TRUE</span>, <span class="at">shade.post =</span> <span class="cn">FALSE</span>,</span>
<span id="cb346-2"><a href="#cb346-2" tabindex="-1"></a>         <span class="at">main =</span> <span class="st">&quot;&quot;</span>, <span class="at">ylab =</span> <span class="st">&quot;Satisfaction with Democracy (All treated)&quot;</span>, </span>
<span id="cb346-3"><a href="#cb346-3" tabindex="-1"></a>         <span class="at">xlab =</span> <span class="st">&quot;Time relative to treatment (Years)&quot;</span>, <span class="at">xlim =</span> <span class="fu">c</span>(<span class="sc">-</span><span class="dv">31</span>,<span class="dv">8</span>), <span class="at">ylim =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>),</span>
<span id="cb346-4"><a href="#cb346-4" tabindex="-1"></a>         <span class="at">theme.bw =</span> <span class="cn">TRUE</span>) <span class="sc">+</span></span>
<span id="cb346-5"><a href="#cb346-5" tabindex="-1"></a>       <span class="fu">theme</span>( <span class="at">panel.grid.major =</span> <span class="fu">element_blank</span>(), <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb346-6"><a href="#cb346-6" tabindex="-1"></a>              <span class="at">panel.background =</span> <span class="fu">element_blank</span>(), <span class="at">axis.line =</span> <span class="fu">element_line</span>(<span class="at">colour =</span> <span class="st">&quot;black&quot;</span>))  <span class="sc">+</span></span>
<span id="cb346-7"><a href="#cb346-7" tabindex="-1"></a>      <span class="fu">scale_color_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;black&quot;</span>, <span class="st">&quot;gray87&quot;</span>, <span class="st">&quot;gray40&quot;</span>)) <span class="sc">+</span> <span class="co">#first is the synth, then raw data, then actual</span></span>
<span id="cb346-8"><a href="#cb346-8" tabindex="-1"></a>     <span class="fu">scale_linetype_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;dashed&quot;</span>,<span class="st">&quot;solid&quot;</span>, <span class="st">&quot;solid&quot;</span>))</span></code></pre></div>
<div class="sourceCode" id="cb347"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb347-1"><a href="#cb347-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_6_counterfactual.pdf&quot;</span>, <span class="at">width =</span> <span class="fl">6.5</span>, <span class="at">height =</span> <span class="dv">5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
<pre><code>## Warning: Removed 55 rows containing missing values (`geom_line()`).</code></pre>
<div class="sourceCode" id="cb349"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb349-1"><a href="#cb349-1" tabindex="-1"></a>p2 <span class="ot">&lt;-</span>    <span class="fu">plot</span>(synth_mod, <span class="at">type =</span> <span class="st">&quot;gap&quot;</span>, <span class="at">raw =</span> <span class="st">&quot;all&quot;</span>, <span class="at">legendOff =</span> <span class="cn">TRUE</span>, <span class="at">xlim =</span> <span class="fu">c</span>(<span class="sc">-</span><span class="dv">31</span>,<span class="dv">8</span>), <span class="at">ylim =</span> <span class="fu">c</span>(<span class="sc">-</span>.<span class="dv">5</span>,.<span class="dv">2</span>), </span>
<span id="cb349-2"><a href="#cb349-2" tabindex="-1"></a>         <span class="at">main=</span><span class="st">&quot;&quot;</span>, <span class="at">ylab =</span> <span class="st">&quot;Gaps in Satisfaction: Synthetic minus Actual (All treated)&quot;</span>,</span>
<span id="cb349-3"><a href="#cb349-3" tabindex="-1"></a>         <span class="at">xlab =</span> <span class="st">&quot;Time relative to treatment (Years)&quot;</span>, </span>
<span id="cb349-4"><a href="#cb349-4" tabindex="-1"></a>         <span class="at">theme.bw =</span> <span class="cn">TRUE</span>) <span class="sc">+</span></span>
<span id="cb349-5"><a href="#cb349-5" tabindex="-1"></a>      <span class="fu">theme</span>( <span class="at">panel.grid.major =</span> <span class="fu">element_blank</span>(), <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb349-6"><a href="#cb349-6" tabindex="-1"></a>             <span class="at">panel.background =</span> <span class="fu">element_blank</span>(), <span class="at">axis.line =</span> <span class="fu">element_line</span>(<span class="at">colour =</span> <span class="st">&quot;black&quot;</span>))  </span></code></pre></div>
<p><img src="data:image/png;base64,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" width="672" /></p>
<div class="sourceCode" id="cb350"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb350-1"><a href="#cb350-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_6_gap.pdf&quot;</span>, <span class="at">width =</span> <span class="fl">6.5</span>, <span class="at">height =</span> <span class="dv">5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
</div>
<div id="country-specific-figures" class="section level3">
<h3>Country-specific figures</h3>
<p>Now the country-specific results can be plotted</p>
<div class="sourceCode" id="cb351"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb351-1"><a href="#cb351-1" tabindex="-1"></a>p3 <span class="ot">&lt;-</span>     <span class="fu">plot</span>(synth_mod, <span class="at">type =</span> <span class="st">&quot;counterfactual&quot;</span>, <span class="at">id =</span> <span class="st">&quot;Greece&quot;</span>, <span class="at">raw =</span> <span class="st">&quot;all&quot;</span>, <span class="at">legendOff =</span> <span class="cn">TRUE</span>, <span class="at">shade.post =</span> <span class="cn">FALSE</span>,</span>
<span id="cb351-2"><a href="#cb351-2" tabindex="-1"></a>         <span class="at">main =</span> <span class="st">&quot;&quot;</span>, <span class="at">ylab =</span> <span class="st">&quot;Satisfaction with Democracy (Greece)&quot;</span>,  <span class="at">xlab =</span> <span class="st">&quot;Year&quot;</span>, <span class="at">ylim =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>),</span>
<span id="cb351-3"><a href="#cb351-3" tabindex="-1"></a>         <span class="at">theme.bw =</span> <span class="cn">TRUE</span>)<span class="sc">+</span></span>
<span id="cb351-4"><a href="#cb351-4" tabindex="-1"></a>      <span class="fu">theme</span>( <span class="at">panel.grid.major =</span> <span class="fu">element_blank</span>(), <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb351-5"><a href="#cb351-5" tabindex="-1"></a>             <span class="at">panel.background =</span> <span class="fu">element_blank</span>(), <span class="at">axis.line =</span> <span class="fu">element_line</span>(<span class="at">colour =</span> <span class="st">&quot;black&quot;</span>))  <span class="sc">+</span></span>
<span id="cb351-6"><a href="#cb351-6" tabindex="-1"></a>      <span class="fu">scale_color_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;black&quot;</span>, <span class="st">&quot;gray87&quot;</span>, <span class="st">&quot;gray40&quot;</span>)) <span class="sc">+</span> <span class="co">#first is the synth, then raw data, then actual</span></span>
<span id="cb351-7"><a href="#cb351-7" tabindex="-1"></a>      <span class="fu">scale_linetype_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;dashed&quot;</span>,<span class="st">&quot;solid&quot;</span>, <span class="st">&quot;solid&quot;</span>))</span></code></pre></div>
<div class="sourceCode" id="cb352"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb352-1"><a href="#cb352-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_7_greece_counterfactual.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">5</span>, <span class="at">height =</span> <span class="dv">5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
<pre><code>## Warning: Removed 509 rows containing missing values (`geom_line()`).</code></pre>
<div class="sourceCode" id="cb354"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb354-1"><a href="#cb354-1" tabindex="-1"></a>p4 <span class="ot">&lt;-</span>    <span class="fu">plot</span>(synth_mod, <span class="at">type =</span> <span class="st">&quot;gap&quot;</span>, <span class="at">id =</span> <span class="st">&quot;Greece&quot;</span>, <span class="at">raw =</span> <span class="st">&quot;all&quot;</span>, <span class="at">legendOff =</span> <span class="cn">TRUE</span>, <span class="at">xlim =</span> <span class="fu">c</span>(<span class="sc">-</span><span class="dv">31</span>,<span class="dv">15</span>), <span class="at">ylim =</span> <span class="fu">c</span>(<span class="sc">-</span>.<span class="dv">5</span>,.<span class="dv">2</span>), </span>
<span id="cb354-2"><a href="#cb354-2" tabindex="-1"></a>              <span class="at">main=</span><span class="st">&quot;&quot;</span>, <span class="at">ylab =</span> <span class="st">&quot;Gaps in Satisfaction: Synthetic minus Actual (Greece)&quot;</span>, <span class="at">xlab =</span> <span class="st">&quot;Time relative to treatment (Years)&quot;</span>, </span>
<span id="cb354-3"><a href="#cb354-3" tabindex="-1"></a>              <span class="at">theme.bw =</span> <span class="cn">TRUE</span>)<span class="sc">+</span></span>
<span id="cb354-4"><a href="#cb354-4" tabindex="-1"></a>      <span class="fu">theme</span>( <span class="at">panel.grid.major =</span> <span class="fu">element_blank</span>(), <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb354-5"><a href="#cb354-5" tabindex="-1"></a>             <span class="at">panel.background =</span> <span class="fu">element_blank</span>(), <span class="at">axis.line =</span> <span class="fu">element_line</span>(<span class="at">colour =</span> <span class="st">&quot;black&quot;</span>))  </span></code></pre></div>
<p><img 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" width="672" /></p>
<div class="sourceCode" id="cb355"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb355-1"><a href="#cb355-1" tabindex="-1"></a> <span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_7_greece_gap.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">5</span>, <span class="at">height =</span> <span class="dv">5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
<pre><code>## Warning: Removed 5 rows containing missing values (`geom_line()`).</code></pre>
<div class="sourceCode" id="cb357"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb357-1"><a href="#cb357-1" tabindex="-1"></a> p5 <span class="ot">&lt;-</span> <span class="fu">plot</span>(synth_mod, <span class="at">type =</span> <span class="st">&quot;counterfactual&quot;</span>, <span class="at">id =</span> <span class="st">&quot;Portugal&quot;</span>, <span class="at">raw =</span> <span class="st">&quot;all&quot;</span>, <span class="at">legendOff =</span> <span class="cn">TRUE</span>, <span class="at">shade.post =</span> <span class="cn">FALSE</span>,</span>
<span id="cb357-2"><a href="#cb357-2" tabindex="-1"></a>         <span class="at">main =</span> <span class="st">&quot;&quot;</span>, <span class="at">ylab =</span> <span class="st">&quot;Satisfaction with Democracy (Portugal)&quot;</span>,  <span class="at">xlab =</span> <span class="st">&quot;Year&quot;</span>, <span class="at">ylim =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>),</span>
<span id="cb357-3"><a href="#cb357-3" tabindex="-1"></a>         <span class="at">theme.bw =</span> <span class="cn">TRUE</span>)<span class="sc">+</span></span>
<span id="cb357-4"><a href="#cb357-4" tabindex="-1"></a>      <span class="fu">theme</span>( <span class="at">panel.grid.major =</span> <span class="fu">element_blank</span>(), <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb357-5"><a href="#cb357-5" tabindex="-1"></a>             <span class="at">panel.background =</span> <span class="fu">element_blank</span>(), <span class="at">axis.line =</span> <span class="fu">element_line</span>(<span class="at">colour =</span> <span class="st">&quot;black&quot;</span>)) <span class="sc">+</span></span>
<span id="cb357-6"><a href="#cb357-6" tabindex="-1"></a>      <span class="fu">scale_color_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;black&quot;</span>, <span class="st">&quot;gray87&quot;</span>, <span class="st">&quot;gray40&quot;</span>)) <span class="sc">+</span> <span class="co">#first is the synth, then raw data, then actual</span></span>
<span id="cb357-7"><a href="#cb357-7" tabindex="-1"></a>      <span class="fu">scale_linetype_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;dashed&quot;</span>,<span class="st">&quot;solid&quot;</span>, <span class="st">&quot;solid&quot;</span>))</span></code></pre></div>
<pre><code>## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for linetype is already present.
## Adding another scale for linetype, which will replace the existing scale.</code></pre>
<p><img src="data:image/png;base64,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" width="672" /></p>
<div class="sourceCode" id="cb359"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb359-1"><a href="#cb359-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_7_portugal_counterfactual.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">5</span>, <span class="at">height =</span> <span class="dv">5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
<pre><code>## Warning: Removed 519 rows containing missing values (`geom_line()`).</code></pre>
<div class="sourceCode" id="cb361"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb361-1"><a href="#cb361-1" tabindex="-1"></a>p6 <span class="ot">&lt;-</span>      <span class="fu">plot</span>(synth_mod, <span class="at">type =</span> <span class="st">&quot;gap&quot;</span>, <span class="at">id =</span> <span class="st">&quot;Portugal&quot;</span>, <span class="at">raw =</span> <span class="st">&quot;all&quot;</span>, <span class="at">legendOff =</span> <span class="cn">TRUE</span>, <span class="at">xlim =</span> <span class="fu">c</span>(<span class="sc">-</span><span class="dv">31</span>,<span class="dv">15</span>), <span class="at">ylim =</span> <span class="fu">c</span>(<span class="sc">-</span>.<span class="dv">5</span>,.<span class="dv">2</span>),  </span>
<span id="cb361-2"><a href="#cb361-2" tabindex="-1"></a>         <span class="at">main=</span><span class="st">&quot;&quot;</span>, <span class="at">ylab =</span> <span class="st">&quot;Gaps in Satisfaction: Synthetic minus Actual (Portugal)&quot;</span>, <span class="at">xlab =</span> <span class="st">&quot;Time relative to treatment (Years)&quot;</span>, </span>
<span id="cb361-3"><a href="#cb361-3" tabindex="-1"></a>         <span class="at">theme.bw =</span> <span class="cn">TRUE</span>)<span class="sc">+</span></span>
<span id="cb361-4"><a href="#cb361-4" tabindex="-1"></a>      <span class="fu">theme</span>( <span class="at">panel.grid.major =</span> <span class="fu">element_blank</span>(), <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb361-5"><a href="#cb361-5" tabindex="-1"></a>             <span class="at">panel.background =</span> <span class="fu">element_blank</span>(), <span class="at">axis.line =</span> <span class="fu">element_line</span>(<span class="at">colour =</span> <span class="st">&quot;black&quot;</span>))  </span></code></pre></div>
<p><img 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" width="672" /></p>
<div class="sourceCode" id="cb362"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb362-1"><a href="#cb362-1" tabindex="-1"></a> <span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_7_portugal_gap.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">5</span>, <span class="at">height =</span> <span class="dv">5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
<pre><code>## Warning: Removed 10 rows containing missing values (`geom_line()`).</code></pre>
<div class="sourceCode" id="cb364"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb364-1"><a href="#cb364-1" tabindex="-1"></a>p7 <span class="ot">&lt;-</span>     <span class="fu">plot</span>(synth_mod, <span class="at">type =</span> <span class="st">&quot;counterfactual&quot;</span>, <span class="at">id =</span> <span class="st">&quot;Spain&quot;</span>, <span class="at">raw =</span> <span class="st">&quot;all&quot;</span>, <span class="at">legendOff =</span> <span class="cn">TRUE</span>, <span class="at">shade.post =</span> <span class="cn">FALSE</span>,</span>
<span id="cb364-2"><a href="#cb364-2" tabindex="-1"></a>         <span class="at">main =</span> <span class="st">&quot;&quot;</span>, <span class="at">ylab =</span> <span class="st">&quot;Satisfaction with Democracy (Spain)&quot;</span>,  <span class="at">xlab =</span> <span class="st">&quot;Year&quot;</span>, <span class="at">ylim =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>),</span>
<span id="cb364-3"><a href="#cb364-3" tabindex="-1"></a>         <span class="at">theme.bw =</span> <span class="cn">TRUE</span>)<span class="sc">+</span></span>
<span id="cb364-4"><a href="#cb364-4" tabindex="-1"></a>      <span class="fu">theme</span>( <span class="at">panel.grid.major =</span> <span class="fu">element_blank</span>(), <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb364-5"><a href="#cb364-5" tabindex="-1"></a>             <span class="at">panel.background =</span> <span class="fu">element_blank</span>(), <span class="at">axis.line =</span> <span class="fu">element_line</span>(<span class="at">colour =</span> <span class="st">&quot;black&quot;</span>))  <span class="sc">+</span></span>
<span id="cb364-6"><a href="#cb364-6" tabindex="-1"></a>      <span class="fu">scale_color_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;black&quot;</span>, <span class="st">&quot;gray87&quot;</span>, <span class="st">&quot;gray40&quot;</span>)) <span class="sc">+</span> <span class="co">#first is the synth, then raw data, then actual</span></span>
<span id="cb364-7"><a href="#cb364-7" tabindex="-1"></a>      <span class="fu">scale_linetype_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;dashed&quot;</span>,<span class="st">&quot;solid&quot;</span>, <span class="st">&quot;solid&quot;</span>))</span></code></pre></div>
<pre><code>## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for linetype is already present.
## Adding another scale for linetype, which will replace the existing scale.</code></pre>
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" width="672" /></p>
<div class="sourceCode" id="cb366"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb366-1"><a href="#cb366-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_7_spain_counterfactual.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">5</span>, <span class="at">height =</span> <span class="dv">5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
<pre><code>## Warning: Removed 519 rows containing missing values (`geom_line()`).</code></pre>
<div class="sourceCode" id="cb368"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb368-1"><a href="#cb368-1" tabindex="-1"></a>p8 <span class="ot">&lt;-</span>     <span class="fu">plot</span>(synth_mod, <span class="at">type =</span> <span class="st">&quot;gap&quot;</span>, <span class="at">id =</span> <span class="st">&quot;Spain&quot;</span>, <span class="at">raw =</span> <span class="st">&quot;all&quot;</span>, <span class="at">legendOff =</span> <span class="cn">TRUE</span>, <span class="at">xlim =</span> <span class="fu">c</span>(<span class="sc">-</span><span class="dv">31</span>,<span class="dv">15</span>), <span class="at">ylim =</span> <span class="fu">c</span>(<span class="sc">-</span>.<span class="dv">5</span>,.<span class="dv">2</span>), </span>
<span id="cb368-2"><a href="#cb368-2" tabindex="-1"></a>         <span class="at">main=</span><span class="st">&quot;&quot;</span>, <span class="at">ylab =</span> <span class="st">&quot;Gaps in Satisfaction: Synthetic minus Actual (Spain)&quot;</span>, <span class="at">xlab =</span> <span class="st">&quot;Time relative to treatment (Years)&quot;</span>, </span>
<span id="cb368-3"><a href="#cb368-3" tabindex="-1"></a>         <span class="at">theme.bw =</span> <span class="cn">TRUE</span>)<span class="sc">+</span></span>
<span id="cb368-4"><a href="#cb368-4" tabindex="-1"></a>      <span class="fu">theme</span>( <span class="at">panel.grid.major =</span> <span class="fu">element_blank</span>(), <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb368-5"><a href="#cb368-5" tabindex="-1"></a>             <span class="at">panel.background =</span> <span class="fu">element_blank</span>(), <span class="at">axis.line =</span> <span class="fu">element_line</span>(<span class="at">colour =</span> <span class="st">&quot;black&quot;</span>))  </span></code></pre></div>
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" width="672" /></p>
<div class="sourceCode" id="cb369"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb369-1"><a href="#cb369-1" tabindex="-1"></a> <span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_7_spain_gap.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">5</span>, <span class="at">height =</span> <span class="dv">5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
<pre><code>## Warning: Removed 10 rows containing missing values (`geom_line()`).</code></pre>
<div class="sourceCode" id="cb371"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb371-1"><a href="#cb371-1" tabindex="-1"></a>p9 <span class="ot">&lt;-</span>     <span class="fu">plot</span>(synth_mod, <span class="at">type =</span> <span class="st">&quot;counterfactual&quot;</span>, <span class="at">id =</span> <span class="st">&quot;Mexico&quot;</span>, <span class="at">raw =</span> <span class="st">&quot;all&quot;</span>, <span class="at">legendOff =</span> <span class="cn">TRUE</span>, <span class="at">shade.post =</span> <span class="cn">FALSE</span>,</span>
<span id="cb371-2"><a href="#cb371-2" tabindex="-1"></a>         <span class="at">main =</span> <span class="st">&quot;&quot;</span>, <span class="at">ylab =</span> <span class="st">&quot;Satisfaction with Democracy (Mexico)&quot;</span>,  <span class="at">xlab =</span> <span class="st">&quot;Year&quot;</span>, <span class="at">ylim =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>),</span>
<span id="cb371-3"><a href="#cb371-3" tabindex="-1"></a>         <span class="at">theme.bw =</span> <span class="cn">TRUE</span>)<span class="sc">+</span></span>
<span id="cb371-4"><a href="#cb371-4" tabindex="-1"></a>      <span class="fu">theme</span>( <span class="at">panel.grid.major =</span> <span class="fu">element_blank</span>(), <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb371-5"><a href="#cb371-5" tabindex="-1"></a>             <span class="at">panel.background =</span> <span class="fu">element_blank</span>(), <span class="at">axis.line =</span> <span class="fu">element_line</span>(<span class="at">colour =</span> <span class="st">&quot;black&quot;</span>))  <span class="sc">+</span></span>
<span id="cb371-6"><a href="#cb371-6" tabindex="-1"></a>      <span class="fu">scale_color_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;black&quot;</span>, <span class="st">&quot;gray87&quot;</span>, <span class="st">&quot;gray40&quot;</span>)) <span class="sc">+</span> <span class="co">#first is the synth, then raw data, then actual</span></span>
<span id="cb371-7"><a href="#cb371-7" tabindex="-1"></a>      <span class="fu">scale_linetype_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;dashed&quot;</span>,<span class="st">&quot;solid&quot;</span>, <span class="st">&quot;solid&quot;</span>))</span></code></pre></div>
<pre><code>## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for linetype is already present.
## Adding another scale for linetype, which will replace the existing scale.</code></pre>
<p><img 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" width="672" /></p>
<div class="sourceCode" id="cb373"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb373-1"><a href="#cb373-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_7_mexico_counterfactual.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">5</span>, <span class="at">height =</span> <span class="dv">5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
<pre><code>## Warning: Removed 541 rows containing missing values (`geom_line()`).</code></pre>
<div class="sourceCode" id="cb375"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb375-1"><a href="#cb375-1" tabindex="-1"></a>p10 <span class="ot">&lt;-</span>     <span class="fu">plot</span>(synth_mod, <span class="at">type =</span> <span class="st">&quot;gap&quot;</span>, <span class="at">id =</span> <span class="st">&quot;Mexico&quot;</span>, <span class="at">raw =</span> <span class="st">&quot;all&quot;</span>, <span class="at">legendOff =</span> <span class="cn">TRUE</span>, <span class="at">xlim =</span> <span class="fu">c</span>(<span class="sc">-</span><span class="dv">31</span>,<span class="dv">15</span>), <span class="at">ylim =</span> <span class="fu">c</span>(<span class="sc">-</span>.<span class="dv">5</span>,.<span class="dv">2</span>), </span>
<span id="cb375-2"><a href="#cb375-2" tabindex="-1"></a>         <span class="at">main=</span><span class="st">&quot;&quot;</span>, <span class="at">ylab =</span> <span class="st">&quot;Gaps in Satisfaction: Synthetic minus Actual (Mexico)&quot;</span>, <span class="at">xlab =</span> <span class="st">&quot;Time relative to treatment (Years)&quot;</span>, </span>
<span id="cb375-3"><a href="#cb375-3" tabindex="-1"></a>         <span class="at">theme.bw =</span> <span class="cn">TRUE</span>)<span class="sc">+</span></span>
<span id="cb375-4"><a href="#cb375-4" tabindex="-1"></a>      <span class="fu">theme</span>( <span class="at">panel.grid.major =</span> <span class="fu">element_blank</span>(), <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb375-5"><a href="#cb375-5" tabindex="-1"></a>             <span class="at">panel.background =</span> <span class="fu">element_blank</span>(), <span class="at">axis.line =</span> <span class="fu">element_line</span>(<span class="at">colour =</span> <span class="st">&quot;black&quot;</span>))  </span></code></pre></div>
<p><img src="data:image/png;base64,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" width="672" /></p>
<div class="sourceCode" id="cb376"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb376-1"><a href="#cb376-1" tabindex="-1"></a> <span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_7_mexico_gap.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">5</span>, <span class="at">height =</span> <span class="dv">5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
<pre><code>## Warning: Removed 21 rows containing missing values (`geom_line()`).</code></pre>
</div>
</div>
<div id="appendix-study-two" class="section level2">
<h2>Appendix-Study Two</h2>
<div id="robustness-check-with-alternative-predictors" class="section level3">
<h3>Robustness check with alternative predictors</h3>
<p>Use of covariates rather than lagged dependent variable</p>
<div id="model-estimation-2" class="section level4">
<h4>Model estimation</h4>
<div class="sourceCode" id="cb378"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb378-1"><a href="#cb378-1" tabindex="-1"></a>   synth_robust_predictors <span class="ot">&lt;-</span> <span class="fu">gsynth</span>(Satisfaction_mean <span class="sc">~</span> Quota  <span class="sc">+</span> GDP_capita_logged <span class="sc">+</span> GDP_growth <span class="sc">+</span> Corruption <span class="sc">+</span>  Polity_score,</span>
<span id="cb378-2"><a href="#cb378-2" tabindex="-1"></a>                  <span class="at">data =</span> Synth_analysis, <span class="at">index =</span> <span class="fu">c</span>(<span class="st">&quot;Country&quot;</span>,<span class="st">&quot;Year&quot;</span>), <span class="at">force =</span> <span class="st">&quot;two-way&quot;</span>, <span class="at">CV =</span> <span class="cn">TRUE</span>, <span class="at">r =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">3</span>),</span>
<span id="cb378-3"><a href="#cb378-3" tabindex="-1"></a>                  <span class="at">se =</span> <span class="cn">TRUE</span>, <span class="at">estimator =</span> <span class="st">&quot;ife&quot;</span>, <span class="at">inference =</span> <span class="st">&quot;parametric&quot;</span>, <span class="at">EM =</span> <span class="cn">TRUE</span>,</span>
<span id="cb378-4"><a href="#cb378-4" tabindex="-1"></a>                  <span class="at">nboots =</span> <span class="dv">1000</span>, <span class="at">parallel =</span> <span class="cn">FALSE</span>,</span>
<span id="cb378-5"><a href="#cb378-5" tabindex="-1"></a>                  <span class="at">min.T0 =</span> <span class="dv">5</span>) <span class="co">#minimum pre-treatment</span></span></code></pre></div>
<pre><code>## Cross-validating ... 
##  r = 0; sigma2 = 0.00628; IC = -4.24272; PC = 0.00546; MSPE = 0.01035*
##  r = 1; sigma2 = 0.00475; IC = -3.76428; PC = 0.00599; MSPE = 0.01722
##  r = 2; sigma2 = 0.00339; IC = -3.36654; PC = 0.00562; MSPE = 0.01252
##  r = 3; sigma2 = 0.00265; IC = -2.90099; PC = 0.00545; MSPE = 0.01274
## 
##  r* = 0
## 
## 
Bootstrapping ...
## ..........</code></pre>
<div class="sourceCode" id="cb380"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb380-1"><a href="#cb380-1" tabindex="-1"></a><span class="fu">print</span>(synth_robust_predictors)</span></code></pre></div>
<pre><code>## Call:
## gsynth.formula(formula = Satisfaction_mean ~ Quota + GDP_capita_logged + 
##     GDP_growth + Corruption + Polity_score, data = Synth_analysis, 
##     index = c(&quot;Country&quot;, &quot;Year&quot;), force = &quot;two-way&quot;, r = c(0, 
##         3), CV = TRUE, EM = TRUE, estimator = &quot;ife&quot;, se = TRUE, 
##     nboots = 1000, inference = &quot;parametric&quot;, parallel = FALSE, 
##     min.T0 = 5)
## 
## Average Treatment Effect on the Treated:
##         Estimate   S.E. CI.lower CI.upper   p.value
## ATT.avg  -0.1473 0.0271  -0.2004 -0.09424 5.391e-08
## 
##    ~ by Period (including Pre-treatment Periods):
##            ATT    S.E.   CI.lower  CI.upper   p.value n.Treated
## -26  0.0646354 0.03578 -5.483e-03  0.134754 7.081e-02         0
## -25  0.1091072 0.04411  2.265e-02  0.195560 1.338e-02         0
## -24  0.0964758 0.04917  9.606e-05  0.192856 4.977e-02         0
## -23  0.0325508 0.02638 -1.916e-02  0.084261 2.173e-01         0
## -22  0.0465659 0.02644 -5.261e-03  0.098393 7.824e-02         0
## -21  0.0120486 0.02691 -4.070e-02  0.064794 6.544e-01         0
## -20 -0.0378361 0.01513 -6.749e-02 -0.008178 1.240e-02         0
## -19  0.0188298 0.02650 -3.310e-02  0.070762 4.773e-01         0
## -18  0.0378823 0.02626 -1.358e-02  0.089346 1.491e-01         0
## -17  0.0265176 0.02076 -1.418e-02  0.067211 2.015e-01         0
## -16 -0.0002716 0.02732 -5.381e-02  0.053272 9.921e-01         0
## -15 -0.0031053 0.03566 -7.300e-02  0.066785 9.306e-01         0
## -14 -0.0526899 0.03368 -1.187e-01  0.013320 1.177e-01         0
## -13 -0.0666982 0.03571 -1.367e-01  0.003296 6.181e-02         0
## -12  0.0440934 0.04722 -4.846e-02  0.136642 3.504e-01         0
## -11 -0.0076238 0.04123 -8.843e-02  0.073185 8.533e-01         0
## -10 -0.0333821 0.04034 -1.125e-01  0.045687 4.080e-01         0
## -9   0.0442773 0.03123 -1.694e-02  0.105497 1.563e-01         0
## -8   0.0484232 0.03793 -2.591e-02  0.122760 2.017e-01         0
## -7   0.0208714 0.05197 -8.100e-02  0.122740 6.880e-01         0
## -6   0.0122277 0.03281 -5.207e-02  0.076526 7.094e-01         0
## -5  -0.0041127 0.03332 -6.943e-02  0.061202 9.018e-01         0
## -4  -0.0658349 0.04891 -1.617e-01  0.030028 1.783e-01         0
## -3  -0.0479349 0.04909 -1.442e-01  0.048280 3.288e-01         0
## -2  -0.0313921 0.03357 -9.720e-02  0.034413 3.498e-01         0
## -1  -0.0399373 0.02967 -9.810e-02  0.018224 1.784e-01         0
## 0   -0.1487306 0.04348 -2.340e-01 -0.063509 6.249e-04         0
## 1   -0.1576283 0.03834 -2.328e-01 -0.082487 3.930e-05         4
## 2   -0.0728629 0.05271 -1.762e-01  0.030444 1.669e-01         4
## 3   -0.1585923 0.03860 -2.343e-01 -0.082932 3.985e-05         4
## 4   -0.1454246 0.04831 -2.401e-01 -0.050747 2.608e-03         3
## 5   -0.2368285 0.05311 -3.409e-01 -0.132737 8.223e-06         3
## 6   -0.2303113 0.03944 -3.076e-01 -0.153015 5.224e-09         3
## 7   -0.0235207 0.08325 -1.867e-01  0.139644 7.775e-01         1
## 8   -0.0670413 0.08107 -2.259e-01  0.091849 4.083e-01         1
## 9   -0.0664304 0.07311 -2.097e-01  0.076868 3.636e-01         1
## 10         NaN      NA        NaN        NA       NaN         0
## 11  -0.1325343 0.10913 -3.464e-01  0.081351 2.246e-01         1
## 12         NaN      NA        NaN        NA       NaN         0
## 
## Coefficients for the Covariates:
##                       beta    S.E. CI.lower CI.upper   p.value
## GDP_capita_logged  0.18918 0.13354 -0.07255   0.4509 1.566e-01
## GDP_growth         0.19426 0.04654  0.10305   0.2855 2.988e-05
## Corruption        -0.07785 0.10726 -0.28807   0.1324 4.679e-01
## Polity_score      -0.39239 0.11730 -0.62230  -0.1625 8.224e-04</code></pre>
<div class="sourceCode" id="cb382"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb382-1"><a href="#cb382-1" tabindex="-1"></a><span class="fu">saveRDS</span>(synth_robust_predictors, <span class="st">&quot;synth_mod_robust.rds&quot;</span>)</span></code></pre></div>
</div>
<div id="model-main-results-1" class="section level4">
<h4>Model main results</h4>
<div class="sourceCode" id="cb383"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb383-1"><a href="#cb383-1" tabindex="-1"></a>    synth_robust_predictors<span class="sc">$</span>est.att</span></code></pre></div>
<pre><code>##               ATT       S.E.      CI.lower     CI.upper      p.value n.Treated
## -26  0.0646353819 0.03577555 -5.483404e-03  0.134754168 7.081038e-02         0
## -25  0.1091071940 0.04410941  2.265434e-02  0.195560043 1.337749e-02         0
## -24  0.0964758488 0.04917427  9.605781e-05  0.192855640 4.977210e-02         0
## -23  0.0325508260 0.02638340 -1.915969e-02  0.084261340 2.172917e-01         0
## -22  0.0465658599 0.02644276 -5.261002e-03  0.098392722 7.823741e-02         0
## -21  0.0120485843 0.02691118 -4.069635e-02  0.064793521 6.543576e-01         0
## -20 -0.0378361467 0.01513192 -6.749416e-02 -0.008178137 1.240461e-02         0
## -19  0.0188298235 0.02649650 -3.310236e-02  0.070762008 4.772991e-01         0
## -18  0.0378822725 0.02625762 -1.358172e-02  0.089346266 1.491007e-01         0
## -17  0.0265175808 0.02076219 -1.417555e-02  0.067210717 2.015296e-01         0
## -16 -0.0002715667 0.02731858 -5.381500e-02  0.053271864 9.920686e-01         0
## -15 -0.0031053115 0.03565909 -7.299585e-02  0.066785225 9.306053e-01         0
## -14 -0.0526899253 0.03367900 -1.186996e-01  0.013319711 1.177063e-01         0
## -13 -0.0666981583 0.03571204 -1.366925e-01  0.003296152 6.180865e-02         0
## -12  0.0440933805 0.04721967 -4.845546e-02  0.136642224 3.504108e-01         0
## -11 -0.0076238044 0.04122967 -8.843248e-02  0.073184873 8.532991e-01         0
## -10 -0.0333821219 0.04034204 -1.124511e-01  0.045686830 4.079666e-01         0
## -9   0.0442773070 0.03123499 -1.694215e-02  0.105496762 1.563208e-01         0
## -8   0.0484231607 0.03792769 -2.591375e-02  0.122760068 2.017000e-01         0
## -7   0.0208713546 0.05197472 -8.099723e-02  0.122739940 6.880024e-01         0
## -6   0.0122276614 0.03280587 -5.207065e-02  0.076525976 7.093510e-01         0
## -5  -0.0041126668 0.03332434 -6.942718e-02  0.061201842 9.017798e-01         0
## -4  -0.0658349378 0.04891032 -1.616974e-01  0.030027519 1.782916e-01         0
## -3  -0.0479348840 0.04909030 -1.441501e-01  0.048280339 3.288349e-01         0
## -2  -0.0313921042 0.03357455 -9.719702e-02  0.034412808 3.497899e-01         0
## -1  -0.0399372841 0.02967452 -9.809827e-02  0.018223697 1.783527e-01         0
## 0   -0.1487306159 0.04348131 -2.339524e-01 -0.063508817 6.249134e-04         0
## 1   -0.1576282954 0.03833790 -2.327692e-01 -0.082487384 3.930079e-05         4
## 2   -0.0728629461 0.05270853 -1.761698e-01  0.030443869 1.668566e-01         4
## 3   -0.1585922603 0.03860269 -2.342522e-01 -0.082932369 3.985460e-05         4
## 4   -0.1454246447 0.04830606 -2.401028e-01 -0.050746501 2.608314e-03         3
## 5   -0.2368285353 0.05310885 -3.409200e-01 -0.132737097 8.222611e-06         3
## 6   -0.2303113131 0.03943763 -3.076077e-01 -0.153014972 5.223632e-09         3
## 7   -0.0235206826 0.08324897 -1.866857e-01  0.139644293 7.775339e-01         1
## 8   -0.0670412788 0.08106820 -2.259320e-01  0.091849479 4.082519e-01         1
## 9   -0.0664303757 0.07311296 -2.097291e-01  0.076868391 3.635617e-01         1
## 10            NaN         NA           NaN           NA          NaN         0
## 11  -0.1325342609 0.10912707 -3.464194e-01  0.081350858 2.245588e-01         1
## 12            NaN         NA           NaN           NA          NaN         0</code></pre>
<div class="sourceCode" id="cb385"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb385-1"><a href="#cb385-1" tabindex="-1"></a>    synth_robust_predictors<span class="sc">$</span>est.avg</span></code></pre></div>
<pre><code>##           Estimate       S.E.   CI.lower    CI.upper      p.value
## ATT.avg -0.1473422 0.02709535 -0.2004481 -0.09423625 5.390836e-08</code></pre>
<div class="sourceCode" id="cb387"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb387-1"><a href="#cb387-1" tabindex="-1"></a>    synth_robust_predictors<span class="sc">$</span>est.beta</span></code></pre></div>
<pre><code>##                          beta       S.E.    CI.lower   CI.upper      p.value
## GDP_capita_logged  0.18918129 0.13353921 -0.07255075  0.4509133 1.565788e-01
## GDP_growth         0.19425885 0.04653582  0.10305032  0.2854674 2.987804e-05
## Corruption        -0.07785086 0.10725642 -0.28806959  0.1323679 4.679377e-01
## Polity_score      -0.39239424 0.11730278 -0.62230347 -0.1624850 8.224102e-04</code></pre>
</div>
<div id="weights-for-control-units" class="section level4">
<h4>Weights for control units</h4>
<div class="sourceCode" id="cb389"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb389-1"><a href="#cb389-1" tabindex="-1"></a>  <span class="co">#weights for each control unit</span></span>
<span id="cb389-2"><a href="#cb389-2" tabindex="-1"></a>    synth_robust_predictors<span class="sc">$</span>wgt.implied</span></code></pre></div>
<pre><code>## NULL</code></pre>
<div class="sourceCode" id="cb391"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb391-1"><a href="#cb391-1" tabindex="-1"></a>    <span class="fu">sort</span>(synth_robust_predictors<span class="sc">$</span>wgt.implied[,<span class="dv">1</span>]) <span class="co">#Greece</span></span></code></pre></div>
<pre><code>## NULL</code></pre>
<div class="sourceCode" id="cb393"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb393-1"><a href="#cb393-1" tabindex="-1"></a>    <span class="fu">sort</span>(synth_robust_predictors<span class="sc">$</span>wgt.implied[,<span class="dv">2</span>]) <span class="co">#Portugal</span></span></code></pre></div>
<pre><code>## NULL</code></pre>
<div class="sourceCode" id="cb395"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb395-1"><a href="#cb395-1" tabindex="-1"></a>    <span class="fu">sort</span>(synth_robust_predictors<span class="sc">$</span>wgt.implied[,<span class="dv">3</span>]) <span class="co">#Spain</span></span></code></pre></div>
<pre><code>## NULL</code></pre>
</div>
</div>
<div id="tables" class="section level3">
<h3>Tables</h3>
<p>Information for tables in the Appendix</p>
<div id="average-treatment-effect-att-table-table-a7" class="section level4">
<h4>Average treatment effect (ATT) table-Table A7</h4>
<div class="sourceCode" id="cb397"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb397-1"><a href="#cb397-1" tabindex="-1"></a><span class="fu">stargazer</span>(<span class="fu">print</span>(synth_mod))</span></code></pre></div>
<pre><code>## Call:
## gsynth.formula(formula = Satisfaction_mean ~ Quota + Satisfaction_diff, 
##     data = Synth_analysis, index = c(&quot;Country&quot;, &quot;Year&quot;), force = &quot;two-way&quot;, 
##     r = c(0, 3), CV = TRUE, EM = TRUE, estimator = &quot;ife&quot;, se = TRUE, 
##     nboots = 1000, inference = &quot;parametric&quot;, parallel = FALSE, 
##     min.T0 = 5)
## 
## Average Treatment Effect on the Treated:
##         Estimate    S.E. CI.lower CI.upper   p.value
## ATT.avg  -0.1046 0.02441  -0.1525 -0.05678 1.822e-05
## 
##    ~ by Period (including Pre-treatment Periods):
##            ATT     S.E.  CI.lower   CI.upper   p.value n.Treated
## -26  1.458e-02 0.005119  0.004548  2.461e-02 4.393e-03         0
## -25  3.637e-02 0.015261  0.006462  6.628e-02 1.715e-02         0
## -24  2.067e-02 0.012606 -0.004035  4.538e-02 1.010e-01         0
## -23  2.220e-02 0.012511 -0.002326  4.672e-02 7.606e-02         0
## -22 -3.763e-03 0.009234 -0.021860  1.433e-02 6.836e-01         0
## -21 -5.276e-03 0.008772 -0.022468  1.192e-02 5.475e-01         0
## -20 -2.297e-02 0.008112 -0.038868 -7.070e-03 4.632e-03         0
## -19 -1.597e-02 0.008156 -0.031957  1.373e-05 5.020e-02         0
## -18  1.797e-02 0.011364 -0.004298  4.025e-02 1.137e-01         0
## -17  3.182e-02 0.008016  0.016105  4.753e-02 7.214e-05         0
## -16  2.544e-02 0.007029  0.011665  3.922e-02 2.951e-04         0
## -15 -6.847e-05 0.013882 -0.027277  2.714e-02 9.961e-01         0
## -14  3.429e-03 0.007189 -0.010661  1.752e-02 6.334e-01         0
## -13 -3.077e-02 0.011020 -0.052372 -9.173e-03 5.233e-03         0
## -12 -1.443e-02 0.012697 -0.039316  1.046e-02 2.558e-01         0
## -11 -2.465e-02 0.010971 -0.046157 -3.152e-03 2.462e-02         0
## -10 -3.444e-02 0.015235 -0.064297 -4.575e-03 2.381e-02         0
## -9  -1.665e-02 0.012988 -0.042104  8.806e-03 1.999e-01         0
## -8  -3.030e-03 0.015099 -0.032624  2.656e-02 8.409e-01         0
## -7   1.432e-02 0.012734 -0.010635  3.928e-02 2.607e-01         0
## -6   4.791e-03 0.012848 -0.020390  2.997e-02 7.092e-01         0
## -5   1.019e-03 0.012608 -0.023693  2.573e-02 9.356e-01         0
## -4  -9.447e-03 0.011352 -0.031696  1.280e-02 4.053e-01         0
## -3   8.980e-04 0.012448 -0.023500  2.530e-02 9.425e-01         0
## -2   1.774e-02 0.009754 -0.001373  3.686e-02 6.889e-02         0
## -1   2.917e-02 0.010749  0.008102  5.024e-02 6.653e-03         0
## 0   -5.912e-02 0.016219 -0.090905 -2.733e-02 2.676e-04         0
## 1   -4.065e-02 0.023768 -0.087232  5.936e-03 8.723e-02         4
## 2   -1.210e-02 0.030649 -0.072171  4.797e-02 6.930e-01         4
## 3   -5.500e-02 0.029154 -0.112141  2.140e-03 5.922e-02         4
## 4   -8.912e-02 0.035139 -0.157991 -2.025e-02 1.121e-02         3
## 5   -1.453e-01 0.035404 -0.214731 -7.595e-02 4.039e-05         3
## 6   -2.415e-01 0.030628 -0.301572 -1.815e-01 3.109e-15         3
## 7   -1.391e-01 0.058807 -0.254313 -2.379e-02 1.805e-02         1
## 8   -1.897e-01 0.072245 -0.331287 -4.809e-02 8.648e-03         1
## 9   -1.875e-01 0.070211 -0.325110 -4.989e-02 7.574e-03         1
## 10         NaN       NA       NaN         NA       NaN         0
## 11  -2.405e-01 0.062957 -0.363883 -1.171e-01 1.335e-04         1
## 12         NaN       NA       NaN         NA       NaN         0
## 
## Coefficients for the Covariates:
##                     beta    S.E. CI.lower CI.upper p.value
## Satisfaction_diff 0.5082 0.02093   0.4672   0.5492       0
## 
## % Table created by stargazer v.5.2.3 by Marek Hlavac, Social Policy Institute. E-mail: marek.hlavac at gmail.com
## % Date and time: Tue, Jul 02, 2024 - 7:59:56 PM
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}} cccccc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
##  &amp; beta &amp; S.E. &amp; CI.lower &amp; CI.upper &amp; p.value \\ 
## \hline \\[-1.8ex] 
## Satisfaction\_diff &amp; $0.508$ &amp; $0.021$ &amp; $0.467$ &amp; $0.549$ &amp; $0$ \\ 
## \hline \\[-1.8ex] 
## \end{tabular} 
## \end{table}</code></pre>
</div>
<div id="country-weight-table-table-a8" class="section level4">
<h4>Country weight table-Table A8</h4>
<p>Shows the weights for each control unit by treated country</p>
<div class="sourceCode" id="cb399"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb399-1"><a href="#cb399-1" tabindex="-1"></a>  <span class="co">#weights for each control unit</span></span>
<span id="cb399-2"><a href="#cb399-2" tabindex="-1"></a>country.weights <span class="ot">&lt;-</span> synth_mod<span class="sc">$</span>wgt.implied</span>
<span id="cb399-3"><a href="#cb399-3" tabindex="-1"></a><span class="fu">print</span>(country.weights)</span></code></pre></div>
<pre><code>##                      Greece      Mexico     Portugal         Spain
## Austria        -0.014265036 -0.55380908 -0.046568416 -0.4723479527
## Bulgaria       -0.102856548 -0.36007190 -0.068270666 -0.4750355201
## Chile          -0.193049050  0.79498944 -0.460152227  0.3412120431
## Colombia       -0.298255020  1.46119902 -0.934274192  0.7403927279
## Croatia         0.092119325 -0.28288886  0.138521395 -0.0757287429
## Cyprus          0.039791057  0.30057216 -1.053406023  0.4287171755
## Czech Republic  0.087459703 -0.96540517 -0.210865091 -0.6033966400
## Denmark        -0.098991020 -0.84435504 -0.173175473 -0.8513136001
## El Salvador    -0.215851377  1.38184183 -0.392442127  0.7701488739
## Estonia         0.022938249 -0.53989449  0.056445107 -0.4035043270
## Finland         0.031059091 -0.78503497  0.174719231 -0.6006778761
## Germany        -0.020446597 -0.48396295  0.443762743 -0.4784027490
## Guatemala       0.005698886  1.91466909 -0.271626330  1.5984130070
## Hungary         0.111640694 -0.68287150  0.332908557 -0.3860733247
## Ireland         0.118892267  0.74775211 -0.420708636  0.8716758683
## Italy           0.040451814 -0.54200194 -0.326695552 -0.3327388298
## Latvia          0.173999240 -0.25246175  0.532902372  0.0579890664
## Lithuania       0.110761552 -0.07775773  0.251565393  0.1136929961
## Luxembourg     -0.028764998 -0.16019761  0.192951436 -0.2036145184
## Netherlands    -0.014707322  0.05338739  0.159035994 -0.0002699616
## Nicaragua      -0.239856726  1.10935439  0.009690782  0.4618200661
## Slovakia       -0.136521804 -0.68850878 -0.054039154 -0.8058705572
## Sweden         -0.099618849 -1.11384653  0.276099331 -1.1192136181
## Turkey          0.275260032 -0.22920359 -0.153852171  0.3352102843
## United Kingdom  0.104101284 -0.20050968  0.210466968  0.0058075026
## Uruguay        -0.188368818  0.24843194  0.754789516 -0.2231364653</code></pre>
<div class="sourceCode" id="cb401"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb401-1"><a href="#cb401-1" tabindex="-1"></a><span class="fu">stargazer</span>(country.weights)</span></code></pre></div>
<pre><code>## 
## % Table created by stargazer v.5.2.3 by Marek Hlavac, Social Policy Institute. E-mail: marek.hlavac at gmail.com
## % Date and time: Tue, Jul 02, 2024 - 7:59:56 PM
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}} ccccc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
##  &amp; Greece &amp; Mexico &amp; Portugal &amp; Spain \\ 
## \hline \\[-1.8ex] 
## Austria &amp; $$-$0.014$ &amp; $$-$0.554$ &amp; $$-$0.047$ &amp; $$-$0.472$ \\ 
## Bulgaria &amp; $$-$0.103$ &amp; $$-$0.360$ &amp; $$-$0.068$ &amp; $$-$0.475$ \\ 
## Chile &amp; $$-$0.193$ &amp; $0.795$ &amp; $$-$0.460$ &amp; $0.341$ \\ 
## Colombia &amp; $$-$0.298$ &amp; $1.461$ &amp; $$-$0.934$ &amp; $0.740$ \\ 
## Croatia &amp; $0.092$ &amp; $$-$0.283$ &amp; $0.139$ &amp; $$-$0.076$ \\ 
## Cyprus &amp; $0.040$ &amp; $0.301$ &amp; $$-$1.053$ &amp; $0.429$ \\ 
## Czech Republic &amp; $0.087$ &amp; $$-$0.965$ &amp; $$-$0.211$ &amp; $$-$0.603$ \\ 
## Denmark &amp; $$-$0.099$ &amp; $$-$0.844$ &amp; $$-$0.173$ &amp; $$-$0.851$ \\ 
## El Salvador &amp; $$-$0.216$ &amp; $1.382$ &amp; $$-$0.392$ &amp; $0.770$ \\ 
## Estonia &amp; $0.023$ &amp; $$-$0.540$ &amp; $0.056$ &amp; $$-$0.404$ \\ 
## Finland &amp; $0.031$ &amp; $$-$0.785$ &amp; $0.175$ &amp; $$-$0.601$ \\ 
## Germany &amp; $$-$0.020$ &amp; $$-$0.484$ &amp; $0.444$ &amp; $$-$0.478$ \\ 
## Guatemala &amp; $0.006$ &amp; $1.915$ &amp; $$-$0.272$ &amp; $1.598$ \\ 
## Hungary &amp; $0.112$ &amp; $$-$0.683$ &amp; $0.333$ &amp; $$-$0.386$ \\ 
## Ireland &amp; $0.119$ &amp; $0.748$ &amp; $$-$0.421$ &amp; $0.872$ \\ 
## Italy &amp; $0.040$ &amp; $$-$0.542$ &amp; $$-$0.327$ &amp; $$-$0.333$ \\ 
## Latvia &amp; $0.174$ &amp; $$-$0.252$ &amp; $0.533$ &amp; $0.058$ \\ 
## Lithuania &amp; $0.111$ &amp; $$-$0.078$ &amp; $0.252$ &amp; $0.114$ \\ 
## Luxembourg &amp; $$-$0.029$ &amp; $$-$0.160$ &amp; $0.193$ &amp; $$-$0.204$ \\ 
## Netherlands &amp; $$-$0.015$ &amp; $0.053$ &amp; $0.159$ &amp; $$-$0.0003$ \\ 
## Nicaragua &amp; $$-$0.240$ &amp; $1.109$ &amp; $0.010$ &amp; $0.462$ \\ 
## Slovakia &amp; $$-$0.137$ &amp; $$-$0.689$ &amp; $$-$0.054$ &amp; $$-$0.806$ \\ 
## Sweden &amp; $$-$0.100$ &amp; $$-$1.114$ &amp; $0.276$ &amp; $$-$1.119$ \\ 
## Turkey &amp; $0.275$ &amp; $$-$0.229$ &amp; $$-$0.154$ &amp; $0.335$ \\ 
## United Kingdom &amp; $0.104$ &amp; $$-$0.201$ &amp; $0.210$ &amp; $0.006$ \\ 
## Uruguay &amp; $$-$0.188$ &amp; $0.248$ &amp; $0.755$ &amp; $$-$0.223$ \\ 
## \hline \\[-1.8ex] 
## \end{tabular} 
## \end{table}</code></pre>
</div>
</div>
<div id="figures-1" class="section level3">
<h3>Figures</h3>
<div id="quota-treatment-status-figure-figure-a6" class="section level4">
<h4>Quota treatment status figure-Figure A6</h4>
<div class="sourceCode" id="cb403"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb403-1"><a href="#cb403-1" tabindex="-1"></a> <span class="fu">panelview</span>(Satisfaction_mean <span class="sc">~</span> Quota, <span class="at">data =</span> Synth_analysis,  <span class="at">index =</span> <span class="fu">c</span>(<span class="st">&quot;Country&quot;</span>,<span class="st">&quot;Year&quot;</span>),</span>
<span id="cb403-2"><a href="#cb403-2" tabindex="-1"></a>            <span class="at">pre.post =</span> <span class="cn">TRUE</span>, <span class="at">by.timing =</span> <span class="cn">TRUE</span>, </span>
<span id="cb403-3"><a href="#cb403-3" tabindex="-1"></a>            <span class="at">main =</span> <span class="st">&quot;&quot;</span>,</span>
<span id="cb403-4"><a href="#cb403-4" tabindex="-1"></a>            <span class="at">cex.axis =</span> <span class="dv">8</span>,  <span class="at">background =</span> <span class="st">&quot;white&quot;</span>, <span class="at">gridOff =</span> <span class="cn">FALSE</span>, </span>
<span id="cb403-5"><a href="#cb403-5" tabindex="-1"></a>            <span class="at">color =</span> <span class="fu">c</span>(<span class="st">&quot;springgreen3&quot;</span>,<span class="st">&quot;steelblue1&quot;</span>,<span class="st">&quot;indianred2&quot;</span>, <span class="st">&quot;gray60&quot;</span>) )</span></code></pre></div>
<pre><code>## Specified colors in the order of: Controls, Treated (Pre), Treated (Post), Missing.</code></pre>
<p><img src="data:image/png;base64,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" width="672" /></p>
<div class="sourceCode" id="cb405"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb405-1"><a href="#cb405-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_A6.pdf&quot;</span>, <span class="at">width =</span> <span class="fl">11.5</span>, <span class="at">height =</span> <span class="dv">6</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
</div>
<div id="latent-factor-figure-figure-a7" class="section level4">
<h4>Latent factor figure-Figure A7</h4>
<div class="sourceCode" id="cb406"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb406-1"><a href="#cb406-1" tabindex="-1"></a><span class="fu">plot</span>(synth_mod, <span class="at">type =</span> <span class="st">&quot;factors&quot;</span>, <span class="at">xlab=</span><span class="st">&quot;Year&quot;</span>)</span></code></pre></div>
<p><img 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" width="672" /></p>
<div class="sourceCode" id="cb407"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb407-1"><a href="#cb407-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_A7.pdf&quot;</span>, <span class="at">width =</span> <span class="fl">6.5</span>, <span class="at">height =</span> <span class="dv">5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>) </span></code></pre></div>
</div>
</div>
<div id="robust-estimation-figures" class="section level3">
<h3>Robust estimation figures</h3>
<div id="average-figures-1" class="section level4">
<h4>Average figures</h4>
<div class="sourceCode" id="cb408"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb408-1"><a href="#cb408-1" tabindex="-1"></a>robust1 <span class="ot">&lt;-</span>    <span class="fu">plot</span>(synth_robust_predictors, <span class="at">type =</span> <span class="st">&quot;counterfactual&quot;</span>, <span class="at">raw =</span> <span class="st">&quot;all&quot;</span>, <span class="at">legendOff =</span> <span class="cn">TRUE</span>, <span class="at">shade.post =</span> <span class="cn">FALSE</span>,</span>
<span id="cb408-2"><a href="#cb408-2" tabindex="-1"></a>         <span class="at">main =</span> <span class="st">&quot;&quot;</span>, <span class="at">ylab =</span> <span class="st">&quot;Satisfaction with Democracy (All treated)&quot;</span>, </span>
<span id="cb408-3"><a href="#cb408-3" tabindex="-1"></a>         <span class="at">xlab =</span> <span class="st">&quot;Time relative to treatment (Years)&quot;</span>, <span class="at">xlim =</span> <span class="fu">c</span>(<span class="sc">-</span><span class="dv">31</span>,<span class="dv">8</span>), <span class="at">ylim =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>),</span>
<span id="cb408-4"><a href="#cb408-4" tabindex="-1"></a>         <span class="at">theme.bw =</span> <span class="cn">TRUE</span>) <span class="sc">+</span></span>
<span id="cb408-5"><a href="#cb408-5" tabindex="-1"></a>       <span class="fu">theme</span>( <span class="at">panel.grid.major =</span> <span class="fu">element_blank</span>(), <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb408-6"><a href="#cb408-6" tabindex="-1"></a>              <span class="at">panel.background =</span> <span class="fu">element_blank</span>(), <span class="at">axis.line =</span> <span class="fu">element_line</span>(<span class="at">colour =</span> <span class="st">&quot;black&quot;</span>))  <span class="sc">+</span></span>
<span id="cb408-7"><a href="#cb408-7" tabindex="-1"></a>      <span class="fu">scale_color_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;black&quot;</span>, <span class="st">&quot;gray87&quot;</span>, <span class="st">&quot;gray40&quot;</span>)) <span class="sc">+</span> <span class="co">#first is the synth, then raw data, then actual</span></span>
<span id="cb408-8"><a href="#cb408-8" tabindex="-1"></a>     <span class="fu">scale_linetype_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;dashed&quot;</span>,<span class="st">&quot;solid&quot;</span>, <span class="st">&quot;solid&quot;</span>))</span></code></pre></div>
<div class="sourceCode" id="cb409"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb409-1"><a href="#cb409-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_A8_counterfactual.pdf&quot;</span>, <span class="at">width =</span> <span class="fl">6.5</span>, <span class="at">height =</span> <span class="dv">5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
<pre><code>## Warning: Removed 55 rows containing missing values (`geom_line()`).</code></pre>
<div class="sourceCode" id="cb411"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb411-1"><a href="#cb411-1" tabindex="-1"></a>robust2 <span class="ot">&lt;-</span>    <span class="fu">plot</span>(synth_robust_predictors, <span class="at">type =</span> <span class="st">&quot;gap&quot;</span>, <span class="at">raw =</span> <span class="st">&quot;all&quot;</span>, <span class="at">legendOff =</span> <span class="cn">TRUE</span>, <span class="at">xlim =</span> <span class="fu">c</span>(<span class="sc">-</span><span class="dv">31</span>,<span class="dv">8</span>), <span class="at">ylim =</span> <span class="fu">c</span>(<span class="sc">-</span>.<span class="dv">5</span>,.<span class="dv">2</span>), </span>
<span id="cb411-2"><a href="#cb411-2" tabindex="-1"></a>         <span class="at">main=</span><span class="st">&quot;&quot;</span>, <span class="at">ylab =</span> <span class="st">&quot;Gaps in Satisfaction: Synthetic minus Actual (All treated)&quot;</span>,</span>
<span id="cb411-3"><a href="#cb411-3" tabindex="-1"></a>         <span class="at">xlab =</span> <span class="st">&quot;Time relative to treatment (Years)&quot;</span>, </span>
<span id="cb411-4"><a href="#cb411-4" tabindex="-1"></a>         <span class="at">theme.bw =</span> <span class="cn">TRUE</span>) <span class="sc">+</span></span>
<span id="cb411-5"><a href="#cb411-5" tabindex="-1"></a>      <span class="fu">theme</span>( <span class="at">panel.grid.major =</span> <span class="fu">element_blank</span>(), <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb411-6"><a href="#cb411-6" tabindex="-1"></a>             <span class="at">panel.background =</span> <span class="fu">element_blank</span>(), <span class="at">axis.line =</span> <span class="fu">element_line</span>(<span class="at">colour =</span> <span class="st">&quot;black&quot;</span>))  </span></code></pre></div>
<p><img 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" width="672" /></p>
<div class="sourceCode" id="cb412"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb412-1"><a href="#cb412-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_A8_gap.pdf&quot;</span>, <span class="at">width =</span> <span class="fl">6.5</span>, <span class="at">height =</span> <span class="dv">5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
</div>
<div id="country-specific-figures-1" class="section level4">
<h4>Country-specific figures</h4>
<p>Now the country-specific results can be plotted</p>
<div class="sourceCode" id="cb413"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb413-1"><a href="#cb413-1" tabindex="-1"></a>robust3 <span class="ot">&lt;-</span>     <span class="fu">plot</span>(synth_robust_predictors, <span class="at">type =</span> <span class="st">&quot;counterfactual&quot;</span>, <span class="at">id =</span> <span class="st">&quot;Greece&quot;</span>, <span class="at">raw =</span> <span class="st">&quot;all&quot;</span>, <span class="at">legendOff =</span> <span class="cn">TRUE</span>, <span class="at">shade.post =</span> <span class="cn">FALSE</span>,</span>
<span id="cb413-2"><a href="#cb413-2" tabindex="-1"></a>         <span class="at">main =</span> <span class="st">&quot;&quot;</span>, <span class="at">ylab =</span> <span class="st">&quot;Satisfaction with Democracy (Greece)&quot;</span>,  <span class="at">xlab =</span> <span class="st">&quot;Year&quot;</span>, <span class="at">ylim =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>),</span>
<span id="cb413-3"><a href="#cb413-3" tabindex="-1"></a>         <span class="at">theme.bw =</span> <span class="cn">TRUE</span>)<span class="sc">+</span></span>
<span id="cb413-4"><a href="#cb413-4" tabindex="-1"></a>      <span class="fu">theme</span>( <span class="at">panel.grid.major =</span> <span class="fu">element_blank</span>(), <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb413-5"><a href="#cb413-5" tabindex="-1"></a>             <span class="at">panel.background =</span> <span class="fu">element_blank</span>(), <span class="at">axis.line =</span> <span class="fu">element_line</span>(<span class="at">colour =</span> <span class="st">&quot;black&quot;</span>))  <span class="sc">+</span></span>
<span id="cb413-6"><a href="#cb413-6" tabindex="-1"></a>      <span class="fu">scale_color_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;black&quot;</span>, <span class="st">&quot;gray87&quot;</span>, <span class="st">&quot;gray40&quot;</span>)) <span class="sc">+</span> <span class="co">#first is the synth, then raw data, then actual</span></span>
<span id="cb413-7"><a href="#cb413-7" tabindex="-1"></a>      <span class="fu">scale_linetype_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;dashed&quot;</span>,<span class="st">&quot;solid&quot;</span>, <span class="st">&quot;solid&quot;</span>))</span></code></pre></div>
<div class="sourceCode" id="cb414"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb414-1"><a href="#cb414-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_A9_greece_counterfactual.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">5</span>, <span class="at">height =</span> <span class="dv">5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
<pre><code>## Warning: Removed 509 rows containing missing values (`geom_line()`).</code></pre>
<div class="sourceCode" id="cb416"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb416-1"><a href="#cb416-1" tabindex="-1"></a>robust4 <span class="ot">&lt;-</span>    <span class="fu">plot</span>(synth_robust_predictors, <span class="at">type =</span> <span class="st">&quot;gap&quot;</span>, <span class="at">id =</span> <span class="st">&quot;Greece&quot;</span>, <span class="at">raw =</span> <span class="st">&quot;all&quot;</span>, <span class="at">legendOff =</span> <span class="cn">TRUE</span>, <span class="at">xlim =</span> <span class="fu">c</span>(<span class="sc">-</span><span class="dv">31</span>,<span class="dv">15</span>), <span class="at">ylim =</span> <span class="fu">c</span>(<span class="sc">-</span>.<span class="dv">5</span>,.<span class="dv">2</span>), </span>
<span id="cb416-2"><a href="#cb416-2" tabindex="-1"></a>              <span class="at">main=</span><span class="st">&quot;&quot;</span>, <span class="at">ylab =</span> <span class="st">&quot;Gaps in Satisfaction: Synthetic minus Actual (Greece)&quot;</span>, <span class="at">xlab =</span> <span class="st">&quot;Time relative to treatment (Years)&quot;</span>, </span>
<span id="cb416-3"><a href="#cb416-3" tabindex="-1"></a>              <span class="at">theme.bw =</span> <span class="cn">TRUE</span>)<span class="sc">+</span></span>
<span id="cb416-4"><a href="#cb416-4" tabindex="-1"></a>      <span class="fu">theme</span>( <span class="at">panel.grid.major =</span> <span class="fu">element_blank</span>(), <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb416-5"><a href="#cb416-5" tabindex="-1"></a>             <span class="at">panel.background =</span> <span class="fu">element_blank</span>(), <span class="at">axis.line =</span> <span class="fu">element_line</span>(<span class="at">colour =</span> <span class="st">&quot;black&quot;</span>))  </span></code></pre></div>
<p><img src="data:image/png;base64,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" width="672" /></p>
<div class="sourceCode" id="cb417"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb417-1"><a href="#cb417-1" tabindex="-1"></a> <span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_A9_greece_gap.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">5</span>, <span class="at">height =</span> <span class="dv">5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
<pre><code>## Warning: Removed 5 rows containing missing values (`geom_line()`).</code></pre>
<div class="sourceCode" id="cb419"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb419-1"><a href="#cb419-1" tabindex="-1"></a> robust5 <span class="ot">&lt;-</span> <span class="fu">plot</span>(synth_robust_predictors, <span class="at">type =</span> <span class="st">&quot;counterfactual&quot;</span>, <span class="at">id =</span> <span class="st">&quot;Portugal&quot;</span>, <span class="at">raw =</span> <span class="st">&quot;all&quot;</span>, <span class="at">legendOff =</span> <span class="cn">TRUE</span>, <span class="at">shade.post =</span> <span class="cn">FALSE</span>,</span>
<span id="cb419-2"><a href="#cb419-2" tabindex="-1"></a>         <span class="at">main =</span> <span class="st">&quot;&quot;</span>, <span class="at">ylab =</span> <span class="st">&quot;Satisfaction with Democracy (Portugal)&quot;</span>,  <span class="at">xlab =</span> <span class="st">&quot;Year&quot;</span>, <span class="at">ylim =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>),</span>
<span id="cb419-3"><a href="#cb419-3" tabindex="-1"></a>         <span class="at">theme.bw =</span> <span class="cn">TRUE</span>)<span class="sc">+</span></span>
<span id="cb419-4"><a href="#cb419-4" tabindex="-1"></a>      <span class="fu">theme</span>( <span class="at">panel.grid.major =</span> <span class="fu">element_blank</span>(), <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb419-5"><a href="#cb419-5" tabindex="-1"></a>             <span class="at">panel.background =</span> <span class="fu">element_blank</span>(), <span class="at">axis.line =</span> <span class="fu">element_line</span>(<span class="at">colour =</span> <span class="st">&quot;black&quot;</span>)) <span class="sc">+</span></span>
<span id="cb419-6"><a href="#cb419-6" tabindex="-1"></a>      <span class="fu">scale_color_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;black&quot;</span>, <span class="st">&quot;gray87&quot;</span>, <span class="st">&quot;gray40&quot;</span>)) <span class="sc">+</span> <span class="co">#first is the synth, then raw data, then actual</span></span>
<span id="cb419-7"><a href="#cb419-7" tabindex="-1"></a>      <span class="fu">scale_linetype_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;dashed&quot;</span>,<span class="st">&quot;solid&quot;</span>, <span class="st">&quot;solid&quot;</span>))</span></code></pre></div>
<pre><code>## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for linetype is already present.
## Adding another scale for linetype, which will replace the existing scale.</code></pre>
<p><img 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ADK1B7hsLX26KMrA2jJw4yXAmjEoiomVSWoAJSpPcJha+3RR1cF0LJnwS8+kT5QJSfVJKgAlKk9wmFr7dFHVwTQ0qU0rhagNSeECkCZ2iMcttYefXQ1AC1fieh6AVqRoAJQpvYIh621Sx9tTNC1fMRAxRUDtF4zXgDK1C7hsLF26aPrACiHE1cN0FoEFYAytUs4bKxd+ugqAMqixHUDtBJBBaBM7RIOG2uXProGgPIYceUArUNQAShTu4TDxtqlj64AoExCXDtAqxBUAMrULuGwsXbpo/0DlMuHqwdoDYIKQJnaJRw21j59tC1B6/uITYfrB2iF6UwCUKb2CYdttU8f7Ryg/OjqBgAaJej8aqiESdgOAShN+4TDttqnj/YN0DVbp+VqDqCgo9x36yVMwnYIQGnaJxy21T59tGuArjo+Uq4GAWq7Koec2iRshwCUpn3CYVvt00d7Bui6M3TK1SJAPWxmuk4AytQ+4bCt9umjHQN05Tni5WoSoH0ROWdVAOjHr3/npZde+trrvy4oHisc3dPcrbhPOGyrffpovwBd+ynFcrUJUI64AP3VVw5Gz/+kklUCUI6as2inPtqUoHtaJ6NcAlBX79w/uHrq61WsEoBy1JxFO/XRTgG6/kpt5RKA2nr3y2dkPv3yT6a2+8e//M7nh+/frWCVAJSj5izaqY/2CdAN1goulwDU6MkPB1r+xt32zpmhf/IbP2m2BKAcNWfRTn20S4BWfF2FAJSgYoB+9urhaajL8xyWPvMzrlUCUI6as2inPtojQDd53U+5BKCLPvt3WHfnu/+rAPSias6infpohwDd5oWT5RKAbiMBKEfNWbRTH+0PoBu98rxcAtBtJADlqDmL9uqjLQlaw0d1+SkApUgAytSerumltFcf7QyglfkpAKWoGKBPvv1SqK/yB+CnwtE9zXlwT9f0Utqrj/YF0Nr8FIBSVD6I9MIh1B+xh4/mwtE9zXlwT9f0Utqrj3YF0Or8FIBSJABlak/X9FLaq4/2BND6/BSAUiR9oEzt6ZpeSnv10Y4AugI/BaAUCUCZ2tM1vZT26qP9AHQNfgpAKRKAMrWna3op7dZHGxKU5aNV+CkApagGQJ+8N+vdfy99oA2oOYt266OdAHQdfgpAKWID9KNvySBSY2rOot36aB8AXYmfAlCKuAB1B+P5y4jMhaN7mvPgnq7ppbRbH+0CoGvx8zIAjZ7LnioSEaBvHw73nnvx/vD/4V6d1ZR7AShPzVm0Wx/tAaCr8fNCAI2dzZ4qEg2gT149PPOb4d+/HFj6TKUHkQSgLDVn0W59tAOArsfPCzXhY+ezp4pEA+hnL9z7Xj+w84vnf18bMFpFAlCOmrNotz7aBUDr2mHpQn2gNwbQcdzog8Ozy781JADlqDmL9uuj7Qha6KM1DbwUQPFT2lNFygTo0Hr/7IVabXgBKEfNWbRfH7UO0BUb8JcbhcfPaU8VidoHOjbhP7w/cHSmaQUJQDlqzqL9+qhxgK7Kz8tNY0LPak8ViTgK/9rY+zl1hU4YrSEBKEfNWbRfH7UN0HX5ecF5oDcE0A/vH57/6TAM/8UBptKEb0HNWbRfHzUN0JX5eUmAIie2p4pEfRLptfH5ow8Oh3v3D2M0WkMCUI6as2i/PmoZoGvz85JPIiGntqeKRH4W/l+HhvuT18YHkWQeaAtqzqId+2gzgmb7aHV+XvRRzhsCaN//jzM3n7zz4MHLtfgpAGWpOYt27KOWAcovNJ7HRQEKWraniiTL2dG0p2t6Ke3YR80CtIph8Sj2oouJgJbtqSIJQGna0zW9lHbso1YBWqcB3zBAwUb8nioSEaAfv2erllUCUI6as2jHPmoUoJU6QJsGKGDanioS9Ukk77VyT1XpCBWActScRTv2UZsArTWA1DJAIdfvqSIVAvRwGB9NYkoAylFzFu3YR00CtNoAfNMABXy/p4pEfJTzl/90RuZXX3/99W/fP9x7+fXvfKXKsvQCUI6as2jPPtqKoBk+qjeBqW2Aht2ge6pI9BXp9ez5d0Z0fvRChen0AlCOmrNozz5qD6AVJ4C2DlDfuj1VpKxn4Se9PS9qx59PLwDlqDmL9uyjFgFardDGARqcatRJav1HCwDVWY1p0of3B3TWWFJEAMpRcxbt2UfNAbSmQa0D1G/Ex5yk1EUIyh5Esmg5famxqJ0AlKPmLNqzj1oDaFVItA9Q1z7cSRM8L4BQNkCdCFQA2oKas2jPPmoMoHUJ0TxAPfejTtLk3J6g/D7QZ73P0gd6aTVn0Z591BZAK/MhzpsWANqTAGqdxtYI5QL0g8PhTyZePvnhYXip3Dv3ZRT+wmrOol37aKP7keSj2nDYA0BtAxEnOWexMUHZz8IP69h94aWXXnpw/vvsOLG+wkx6AShHzVm0ax81BND6/Iy/RLhqYaPyAepYCDvJd8umCGUDdAg8Z/3b3wwAvVfh3cYCUI6as2hnPnJvv3YAWp0LLQI0tCcBUACXWxK0wmpMH//owfgQ/K/Pn598++/sDtA//OCVR4++8XPz/fuPtP7sX/r+9980n93C8+29lHYGh4tobz5ybr+WAFq5zAYBCqDP2gI4CYbldgitAFBcMyANHz2AfvKKAHQFNWfR3nxUDtDy2zbto/pIaBGgUAi6bAqdhIFyM4KuCtA3H33p5/2n33/0pd96Oz555U//4fznd4/+AikczbK5W3FvcLiE9uajYoAybtukj1YgQnsAhS1aNgVOijhlI4RyAequB/prZ98nr4yh5e+/OdLS0jkQ/evh75vTH6DwfHsvpb3B4RLam49KATrcsmsBdA0cNAjQHnQ4AtA4I7chKHsivbOUnTuF/v05wHzf5+T7U0z6h+/7ZF0Kz7f3UtobHC6hvfmocBRpfhim0KC4j1aBQXMAnWfDI9t9JyUJuQVC1wTom4/+dvzrt9R//81px++/+aX//jePHv3Hn/e+BKAcNWfR3nxUBtDlYZgyg1IALco1rvYAqs0KdkybHCcR6LgBQbmLifzy9Vnf+fLh3t//2BmC1wHmJ6+4naA6MtVjSDNnx0JnnUSii+l4xL9FDprT0ZLniWhDfq7rZIzqF0agQc4fYI+9hWT6kZZsBeUPIk2LMaUBuvSJ/u7Ro7/6bf8/f/BoackLQEUNqASg5kZd4Y5dCQKtAXT5CYJwGWwg/65dhqAFo/Bvuw9xWgB15in9To/K60g0HEuSJjxHzVm0Nx8VNOHttmJRqzG+1GVJjmkl+mw3b8Kr4IO1b9i2OCmnbb5uO77mNCYvBEUi0D983zTZJ/0umOckAOWoOYviPrrEMrgJH2UPw3tPY5cYFF3qsiBDghoDqL0iCLC3N07KY+KqBK0JUG8hOwSgXjwKbhGActScRamFxDczxKguQFVBzOobFANofnYktQZQ1zJ/r1qclF1lVkRo3QiUMgofzp73B5kEoDw1Z1FqIfHtLFmUM+adNDA4hboAXTMAbQig7q9WeOx5/+QkuMaoRfDOMoOTqgjQJ695K4Hq+Z/uPNClx3Npy4dIFYBy1JxFcTi09SqbUXkAhVazyDcItWg197QGUOcLSNDRSSl+whxdy4vcaUzffknrxfsHbyVQ8Ekka/b8mxM4w05RAShLzVkUA2h/mU7QigCFbuiqAM3Oi6jxx6vrQvTMOtYvMgJQz4sgJM9OIiweAnN0pVpWdSK9txT9mYx/HjwL//tvLh2en7wyTGP69G/CZ+UFoBw1Z1Eiuto3QJEbOtsgzKL1vDPyJQbQ+m2DGEB94wCDjyeQn3DD3Q9ImwfoUy/7r/L41FqNaV4/xOnwfH9ejCl4FEkAylFzFiXgcIk2fDWAIsZnnxL+torMjOhSXXwQ6Yj1KJYLB2jYDQJZdCTj0909/lSs4slVV2PqP/3BmY/fGIGpAerMWfr0Pz169Kd/5cefAlCemrMoBYcdAxS9d6sBNDOfDHVdn+oDRUdlYoocEQFomAtg0RHmJ82oVQi6LkBLJQDlqDmLUnBoHKAR+/CbN/ec0Nf95GWTo44yiASOyEQUT48ClNaPDK4HSrVtHYKyB5G+atrtH774b9jv45wLR/fsBg4XVHMWpeBwgTZ8HYDGDK8E0LxccqRIAO1zGJpMiQMUyiy06OQnyWJ7twJB2X2g1tTPGm+EnwtH9+wFDpdUcxZh7wKDPm6k5Oqb+Dd7e7Rxn2dQyke1NbOHNo2JglAKZ3MACpiWtR4ooGHELOuAtGoC1J9IXy4BKEfNWQT6yH34cWuC1gBowuq8c0r6qLKUXsoOLyOkVbQvg4IzDKDISFxgkbecXbZ7xjkHuQdFVQ5QbylQaB5TsQSgHDVnEQwH58sOAZoOybIMSvqorpYBeCpAY5Akt/FRgMI2+pstJxXOEKg+sYARgX4QArTCG42nwtE9+4DDZdWcRfDbaPFvG4gP0PSNmHVSaR9VlSoAaA8zNGeUCQEodSqDcVIpB8fAu6ZfGQB98t+Gx4/uPbc8i/S1n9SySgDKUXMWgW+j9b5uTFA2QCkWMwG6Mj9LANr7vMwcpMcAGjPTtqhoNSY/y6oErdkHWk8CUI6aswiafZLasLKyAApYR7oHc04KAij96Expfpo4FDQJP3xiUO4UJwygka5VzyIzsyqj0DDLigitOY2pngSgHDVnEQDQYMO+AEq8AVkAXTMAnfNXKvZWpPgaWl3JJHsEoJFyXItOXHwuPx61EMqdSP/a8z+tYodXOLpnB3C4uJqzCHidt59k6zY8D6BUazNOivAjU02KDdBpUmW+hSBA6ZPB0MVEcqTPvk6N4z8LX2vgyCkc3dM+HC6v5iwK5z+HafYE0Oh0ntjXmEHpH5lamnNmArTIPhig0XIci6qsbzLnUAeh0gfKlAB0UqwyBvOfM49fQRyAJqZDuhvIBhF+ZOpILQBhArTAQgigGbNpwcVEsqUsgrKz4/aBvnrve1wToMLRPc3hSgA6KloZfYAiGVQ2KaqUjyLD8DFLg+WhyWcVAJR4XL50ziyAjkfmlw0CNH6ItVsd6yxRqowL2LWO2wf69uGZFTpBBaAcXcCi+OQQ10dIsg0BeraUBdDoUT5BiTZRovQqUpUAWmQjANBULvaUqWOlqm2KZCOUC9CP/+lweGqZClprSF4AytElADr+g1VG7wk8JIuNADoNHqdCmbK+TO2G/CNpPzIVZPOICdCCEBQCaOoYK1ysdbOpegTlDyLZkmfhW9D2Fi3DEnBldAGK5rEBQZeZi3kRKBFoxg3Zh/oAJR1TIAcdvYoGkmmAZptZBFDj1Wo3m205D6ECUKYEoL11E8CV0fYReQC7vux53+sA1BQEbI2LFKXz5YCjHKDzGVYAKOFUZ1irmjebT9BiinKb8OtIAMrR5halWkTOGhCUXGpb1bv07FcGKLnP1BIpSmdLVQNokB1JAEAJRy09yxVvNqhylGBUAMqUABQglZfAXgMilk1Vbjh2BPfGGgB1m8fWdsKxxCidKR/sKtoJmgZotqUBQImOnS9eTYCGBRdhVADKlAA0fJ+iX/8sgEbzqQvQhcjQLbEKQO30bqiXFi1KZ8qfWlAKUHN6bICSDut0x0vFqo0PZmZhtAJAP/7Rg8Ph3oOXf00ojigBKEcbWxTWM7/qLT6KV8mq4JhnFGG3wgoA9cK7TILaAE2nLlMYgPalAAWzTMsHKLFrZHz9XV/5ZkvUxVm6aEx8gL69DCF9MV5UhgSgHG0NUGCT115eViGLZ1SToGNWXYctQZ4JUNJYh3eAwnaBonVzsOR3tdQAaCbtA4BSDjK1qS5ACZd0rEDRJGyADvx86rmXXvx8TYIKQDna1iKwHroEnX2UrLEV0TG3T9H3iCV9lB2ChshFukRhEaN0hsIzKgVo5qlZ8gBK7QHVCevebJRrmkzDBeiH9/WjSB+9eqj2WKcAlKNNLYp1JenPGqCleeVrDDznMWYw0xUAGuaQE6cZgCaTFiqMqcsBimcblw9QwiGTG9cAaPIhUkqTiAvQ18x7kJ68eng2WR5NAlCOtgUousNUP70ObjKvGgSdAk9rmKOoCZ8NUHhUl3w4tZujXECnRB2A5hjsApTWs6xMKbUBGiufOIpUczGRD+/LS+Va0JYWxerYUgNHH2WPw5QYo3v+0fEcrfoAhe0hH78ANGVYoQIDBnR2g6eyAeo5lAHQ9AFLWePf2jcbflXIc5nYTyLJe+EvbUGgTQEa3WlN3yN3dxXa4Yy3B+M52aPwuQCF99sETZRH7ScuFDRVYgRoVwLQRNa4cgHqObD6zRbpfyKelQCUqdsGaJorQ4oRoFXyw8pw5yoB4zl+vtUBitpGzIDazVEoqINhAigeghIBmhOCOgDN69PRFamq0BFQ8jmxm/DWivQfyHvhm9B2FhHHMcc3MVTKMMgdmOcZ7Y6clAvQhGXoXoug8fJmgKbMKhTYQzsMtfXYPK8+A6BQdAvLBWjE4Dkbb7Sq/s0Gd11nXAYZRGLqtgFKSTMs40itkvS6i8+RB29fP11tgEbysTvxcJH7iUsEe6QfJolP/aCwSVhmQXdqkAQzxAYoYVzO7WutuBqTk6tfaNZFqDCN6emfjJ9+9WWZxtSGNrOIVtXOAD2S6+YsVX0AACAASURBVCQhSxVh55QAOcr+uhlAqQQl9xMXCGuojgBV+QBNbcHPwwEolsgY6G1Y5WbzIJ3bDV9lIv3hwYMHVR9FEoBytB1Aieky3sSQnlkSZWckC/eYugBNWT1/iJVH7yfOF/aLMpBzDkRBk5DcgJifUN4oC6CE7vNgyyoAtbtZ80cx+Y9yvnN/fpLz3tczy8YlAOVoK4vIlS3DR0gNpqFzTotnbXalLcoZRYrbpMuNZkHvJ84W5tIRnHNXKGgSkluq0yRyHjZA0UR4MbVe6eGVtJRYcAUqLCby5N0XzxHoc9+tNIA0Fo7uaQ5XtwvQjJHKDIugPikqOhNm2VlUBSh1jlPsTAaAJm0qEhaRdxqgw2MHUAoMoNBG9Isrw883aD86rlYBaK9/3op+wCoAdAUJQDnaCqDklGUAzUandzy4U+/NBmgsY8Jwsi4+kE5yIg+0ZQrv0RgDT6XQ0CsHoCqRYJYFUDxRjzZEKr2VMyisGJ98gFZcw84uHN3THK5uFqAZFS4PoKoQnfPxydwnkwiGxL9T9gTFWhs8ZQy0kRXzoANQGFigj7A+FvBjIMPPKEAxq08rOKmP+yklDkCf/HDo/nzq6xXb7rpwdE9zuLpdgNKT0n2kxvWTGBWaOjRRFaAUs2I9sxNA07nQlf4F0tOXJoCCBiIARQpMJJhkAPpWJBVq9RmgaxG09FAGQD/Uo0dPV3r+yCoc3dMcrm4VoDlVjuKj5YZPLcCYyIZQzmgSwaD49/QOoFhc9d7YS4ve9XorM0AhA3MAarZHSzUBaASguOVDP8daUxUKVQ7Q8YWcT/0vwzqgtR5AMoWje5rD1Y0CNKsex33k3fKsW4Q4jbQuQAl24adVrwmf1/HReQAFDMwCqHZDvGwTgOIAjZg/TlVoi6DlAH37ME2cf/cciP6lv5MpAShHmwA0JzEFoPb3MpPGYylphtLqAZRqrneOQB9o8XnnoXPSsooITlDIR7G+iOlPtFQCQGPnMD1t0BRBiwH65FXNzTNJaz3CuRSO7mkOV7cJ0LxKHPVRkBXjBiHPdFIVAUoqsofH4KuMwmeic1II0OAMYYDiZiT2DzIteAyg0ROZn3dtiaAcgM5Pbg6L0lduwwtAOdoCoFmpYz4K7wbGDUI98BzwEdKkNkS2IjlG4sTyifRF/lIAQH0DcgE69gjEizUBKA7QyOF6yaqGCFoM0M9eOMyL15lP1SQA5Wh9izJrcMRHUE6MUIyckjSsRcm91r1c3r1XdJRZSdkBqJMV4KOoiSptiwlAY6PwqJZFU9shqACUqVsEaG79jQG0QvYlx7UH0GNpLFlGXQigngEgQOOWpGwxASgHoC0RVADK1FUBlDoekpkt7iPwRii9PbImBhCyowC01p08DSIVnHmZs+a3Rk0zQJ1JnFZuuQAl2GIC0EgfKD4mZr26tBWCCkCZ2htA4aGMrFHc7MqL+gid2pNZQP5R1QCaUWa0rAGg+UNB5c3+5Xo7AHXyC32UKE11qXJNAEoEqCvzLHwzBBWAMrUzgKb4SamY2VUX8xE+M6goqspJXAmg4UhTjhEml3GltuFP5lMEpRxRU1nTU7POWVg5QgC1jw93d6lyTQBKbMJ7AD3aO2g5rCwBKFP7Amiy2hEQml9zUYDWKyJ7ZlV+hiBAnf1FMeR0oJ6h02GLc2JHZpc2HdfpVex8gFp5ogDFzjRtjuFnWR+o9bRBIwS9GYAWVW2C9gRQ0v2dokCBGxEf4TkVATQrdQFAwRlX85/MGN7NYzxADzB3OY+ylg+3qXk50D58sfFyAjBAl1MMTpQ0iKT5WTiIBMfKFxQHoKEafitnYXSQ1FoAZViL9jh200IdlKIjcCswKO+hwKJ7I3dmVUGOYMgVYDPzwunki4/Gi0Q9NqMgr8zlVUj6oXhn9/g38NE09LQkD45LW6Q7QMsB6hC0JI/KuhWAKoWve8jSmgAtNBfBVZexzhGerMQk0EepGTGZyjyADdAphgP9mXHdTFLjIxV5UaZ/MLGY8ECrjLA0zXT/MP/3V3ke6ZOXwQSgpdOYWiNo+ZNI334p1Febfa3x5OsVELoeQIuDZiByUFbvGg+hJe6DARovPbOI3AMYADUxJ9bYJl83K53lI+LPXHlVHt8Hv5yf9dFkPZrkFxf0Lrg4NUeiMvwsngfaWCOesx7oeloLoNURaqafwGJkbIifm4vjI8AYYpZg0UWnBAE0MSEmtxyWj1AjwO+LM+N9ELSuEmOQMz5CyIDDzzHoXG4JIOCdumVdU6E3fzi1avoTLdrwsxyg9QnKyeVGAOrhoxJCh5ziAC2v5HaXWmn/nrHBy6MYoYV9CpEJMWjRWSXwfmSImQbfY4eQCGglCAaYExkwKnHXObM/oYLGLdasdWUTFzZjyS1SsqoAUH/mQ0k+nlWcXG4GoM6XGn6fa1Vqqbbi3L2SMo7VKy6Y4wAQZiN0uqszzLAMAjoVkgXnFJDv5nUAmvEr5e913lahI9xIDuU1eH4dvA3QsC9irNraTqWbQ6AduoqlDVPqrRoArUxQ7LeBaBK2I9kH+mM0z1+ye0JrA7SUH7EM5yzWAahnXl4YusTESG5LlkRL2AE1AFBCsRkF5JtVAtDevygpOyIOC3a5r/tZkIXlwOKn8gAKvRlejUv+2RcdMUWbajagJau+BkArE1T/WBUezhiFf/qn4IEffZk/Fl8foOEWhuftapVcLLiwhEiRKcuOXlK85hMR2s1Ta0o9FviIdCb0/AvsqgFQygGwj4HN3ut+Yu3rnoMN511yuijoEaK5HtkdC1iObtXAfzT6OgD1XqLMIqhppBVmUN6Ef/tweD5E6K++fDh8sdAWq3B0Ty2AliPUQ1liFL4WQMOCwb2jgtFTvOYTm/HzpJesp2Qs+T6iFJsF0DxzBvEBCv0oQ3kAToa2+QsqW03iMDWPn7p7wJQEzicYAQpZFObpzqBHq1s1gFYjqB1fl+XA6AP96MzKp16232v80XfuHw5Pf6/MEqdwdE8RQCMhWK7bAoqtAtCYwXAwbbewg1H4WDkJ+3S2Ci6bppOPdMIxGYWVmFUBoGF6JBcV3PBAyhCgdg9MLWQsWXkAhU3yD43l6nyFE/W1AOp5muUO174Ck7AdlEGkd8b3cn7hpb9//fXX/++XHgxf7lV5yXF1gKI7spwPQSQJ0LIol2RCgM7ZJHr5CQfYWftxRoZ8gJIOygBonjWj2AClBaDzDvdGBRMGL5zEj+EBAwYo5ET6z55y2yZwr8Xwby2A1iEoMcKOm4TtII3CP3nnvvMkUq13xNcFaCoGo97QYNrURPrKAHWpCVvvzt9LFpXx81L6K+3FxKSDyGUVGcUHaI4Zzs8QnC58pYcPTSsixUuKauLn1ILv7fwUOA6d0W5wuwDALtXx30oABUbrCnILaneRSdgO6jSmj77zYKbnA6c5z1JtgMb3UtyPtmDXAGjKIBSds0nkjJbcsEKCjensIIPcmJh2EPmmqFvvHQvwDbmuURpc2FkB70Ty4iNub93kUR2A+gAFss2IQL1YDpoWNf6pBtAKBA2OqftLnDUP9L333isoG9eWAAViOliIQUmAllza7ENs2ROgacUBdQk+uLCZ4wCUelTtSNURqR5FCJoVgE77oz964Evlgjb9zOB4SRELeg+g7pfApPBoLGuvwyEAqN5bC6DZBAWreJiowCRsx/VMpCfUuGJ8Ep6FvxhAqfwMk0bOmA3QjK5NIkCzzRnEBGiCdUhusQsCvVTO2zAez+anC1Blf/FNcg+PZW3bClmot1QDaHDZ434Jb+Ja8cFNAJRhSVIrALT4Hpml53rk5KKcGwA/tKyZY3cq5JhESlVgEB+gQdIiK2yDTmB5Xi8d9Dw6ScqQc/4TADQong5Qy1aw8iybmAA9mplVSV8F+5RXx8GE2SZdP0C5OEqIANDc8rkG60fwMgtdOuniv+UlBqEtr4RJddJA4gG0JADt474DARpcxazllt18bMrBAPXto87mWPJUKX4yAWoWngiNjXnXnHWM8q6lVN0CQDmWJJVezu4iAC342Uj2VsypCgwyAM0ziJAm35pRTICmEoKZpZrwcLec24rviuKBIG5T4xs9lH6rhwaomzV16M9pusQbxiyAzi+Vo9VT+zCTVKW6QbK9KwBlqj5A2SHzKaf70y2Y0pVXYBA2eJq0J50k35pRlQGazCh514frZAA2uJ2NZNlN1+lr16kJoF1vAbQvAqiFJzg+trZxAKrGN+8tn6m9GcpdB1oBy5q6yfOsunqArtyCJwE0tzFdbMykUxk/iSrpaIfnP1coqvg0ywAKR2oJO21y4lcGA6i9zfSyZBRvV8DlpQTzs7n+wiJuTsT5xJZ9IJpUHYAOJjijkfO5JI4J38OQeGeKANTVyvykrEi/NUCP655zMUAL+jKSaMi2ZRYPoGQrgrATveUjAFXeJz+PNEW0IUssNv/pgJWZLJPsPCLZWwWBnRDWl3KAjjn7T2TgUb3eZTqNzSnGuZtZp64doGsHoPUByrf4uO4plwM0v6jYIaw4mwPQIADFjgZvVcTsE9pzbXETzCMRh5kQzG3IjgGo6u6Uk10BQB07gfO1vxQDdMoXeibY97Kyz9bZYWXkucz7Pcox7PoByrMkKRJAc4xgW6yOzAxS+WcbCCzjyCwqFUakTSorfrr7Uqn6qIHw9oiPlnsezmPkFlRQP3UAmh5AH6DDyvTT6spuJ6k2yc4HKaC3s3TLgI4sBeicK7IqjQrlHGaltlzh/B452eZYduUAXT0AJb1UblOA+svZ1VcZQEvOK9o8y8/ONolYPrAh2QOZtA/aG/uRgeJel3lI5OoNmHgh7PRK+M5djMnKifRIsMUwKK13YDlAJ4v8dRHtFKHbfTA6EalJrBQjBGUC9LN/9yfVHoC3C0f3ZAOUaUlStQHKRr4qfvEeuYRcC+E5jkVF1YDnaFJp8f7vR8g1goFACg1QjKBoV4CComIrKHO2OfnNbdzOTQYDFD+T4ISczLwDCwGqs8lbWDZwZhfwVf/4BKQliwvQF4Y1QSu9zNgqHN2zU4DSzeAHoKsDNP8VmKeCsHUqKajwldoUDIB6W9zdVPuCdNGBNjDbeZvGqG9F0Ox3AlBt6hSIukuLLCYBRwZG6BJdw9zdRmUAXTLMebVB6DLwtcz6Z4iarS8mQJ/8cFyI6env1mVoLYCu34KnvRd+S4CWLbiSV0amjdBCGdSSrC62WvAcTSIWn9oSgItqgJ82PlMB7cuwOvWMDWYbElgt7fdx2/yWziCZ9fwYdlp2EAxYGxxXBFCTfcbLtQB+wr9C88MExGwD8ftAf/WVkaFf+C65zLTqAZRrSVKVAcplhNoCoNlz5U7FZ6UDqZrwHE2ilx7b4nAn00Q3fdFUL2UtfKQCPwX8dAG69AyoO7eHQH9cZnPA0a/SzX93nH/cgh1XAlDLbvrrXcNrocLul3FzpyNxSrahqgwivTsxFHhFUqEqAXSDAJQKUKohFfi5BUCBihix/FQ+sWq5J0szQFQLoGZ7vo3OIQtAMwmq14J3n7eBsnKDzPndxuP/d3deJD39XeYTg7HchE6Qn25QbKsAoICTnN3Jg+wtYJ+M+Skw1pMvQ6VR+Cfvfnl8oUelNZWrAZRtSVIkgNIt4QJ0+Gd9gELLYUYQciqfWLUGPAfVAaghTJmV1mGFT2spQ6+wgw/9PgBUGYCquw7qLD0tIPS4vGxQwV6zHWRbPkChMN3d71kGx8QY1Jcz9I8w3+P21ZvG9PG3pjck1QhDrwageCMISc8zWW0G0OCX3L2zXB1XnplaoCoAtfsdy8wwR+IAjd7Gy70e613QKe3PDkD958pngC5dnIgxc/gGWQWeSQFA3eyhm81tBKD8DJIbQ4Md8+/RlgB98qPPLy9G+pN4iRTVAeg6sYunKEDNTzXRlBr83AKgyGRyuMZNi+i0pToA7RN4IxUx/+otAA1wFSli3K4Hl6MNeBeDI3IN5tTdHRBZ6yWeLWC6hmD8XMgcKBegXvYgQINbLHo9oF/AcAfwHVENgGp6DmPxH/+wnffCb8HPBEBzQ1AmQGeTOHnkFRV8BW52axGddlQDoEpBp5urOQN/xSqfnBhA5/hReWkAfnoAteNEdW7EB/n2eoE97NcRPfnpKMjeTID62YMVyW+cJy6I25Nh78ATRsQG6Nz7aWaDfnB4hj2nqQpANwlAowBVyCoNkQM4Ji+tQUYeuWUB34L7jNhPvKmIFsUISmrhucdihShlAxSMOiOkWh4lcmxD7e7HmNUDaOeGoP0M0AWS0Lmip48GoJkADbKHK5Lbz5C8JJaT4O3IfljceaCvHvzBo89e+KOfEQqOqhJAuWZQFAVov1wW2p1WhZ+bANSbQO5bYt1a6loB2uUGn2jyIaPlSSRslTasqaz/sSobVJILUOUD1Huz+7hZW4QE2viCnAp4cGpSFkDDIhGAWl0JhGtiaiay3WxJ21jjSaR77vSlz15oJgLlmkFRBA5OVLABQBeTGJnQS4t0H9kIHe/EKwSoiq8qCeeFHqDM2yqw5X4xgC5+dhrbYUqvBe8D1B/EV/qJtnGIHjrVCD9RiqUBajewg0yQimQ/TkC4KH6bPyzaTpjIjw3Q53/ibXryY/5UphoA3aYFHwWo9S+JjSyTzbHb4EqBH62N0300tgWvDqCJZc3hvGKVYAYoXCZiigNNHSr2cDVyf++UdeTSQPcJOkegd3d3oEW4AyJrxRMAGmuKYwDtDD+RbN30TkFh0faWVJbXuxrTNvyMwMH9ocv4aSyTOXYjgGJ9SVaKuWV4bQAdTyy3xEQ4k37xHkxFu69k+r0Ca5oH0N4HaBi7Dat6jScK8jP2AxKJzQlN+Nj9ggMUPwYpAjwpKOd1I9CVVAGgGwWgMYC6f1cGqHXsRrhSwYdQU4fedQE0HOChZQXntxiUfPI8bHIu7PNsg451msYwQP0Q9KjQk53fpgSaie+i9YGaHolAcYDSLwrSmwKdZyrLagB9L7E/S398Yut45OdRx4D5Q9qgI8PmC5yuLjJS9HnXcdRGJlVXaPl0OvlnpGtBUaEn8Ngx3eRiY9xj8Gh74/E0n8C0cf40/Pf46B10nP7xL+C5lMeYjcM+eNegXxihafRpkaWvR8YxSJUMPYydZ1pkgD750Rd+No4m1XsUvk4EWsWSpNDoSnmfMoYHC2QfulW8p5w/eIqxf28De3JUHIGSRhbwjFBHWPUI7xIJv+sxcr2pu7tLHKqU+c/k4nUHTCYtR7rbIwNdqeCcNAqv0EF8ZB6oSnQwh0KC5KAXA7XEmITtoAL0g/uHP/rZPBz/l/Gy6OIDdKsWPApQu8oGW5KH5Mo5dDOAJlqly67TMX5bXUA8gBY24J0PvkGE1TfD21sPcxiAgg1oy/uGlxkAdQGMdgzqKU+I+T11GhNaW+AnkVTsEKwAZEdoxcqDSB/eP0wT53/5rfuHe9+LlkVXDYDWsSQpFKDWR2X/QcVgDFDxt1Cifi27TtNwREsIZQG0gJ8q/OQZZAOUcHvbAagz8gJ123m/5gFADSz8H2J7kpQuAuEnBTg0gKIDVOBiIrS7yz0IS+7ctaQsuQB97fC0brk/efXwLKFEitgA3e5uxfq1nd+yeVPcJk4A6ppUnE9BsQR+Lm+cbAih5T/EyABEIhvwo2OQZRGpDb/MRDJ9CpODQ/x4AajXip9BHMZxJ6tIA9IOvozL5eUDFJ/iAK0HOhcXGbkCCsDMtB1Fu8rseaBW1Pnhff4zSHPh6J7yyGEl4Q9HBN9WA6h35HYAjQZjZo9Z67IZhHIAyglAWQD1Wpj90oI34eigAHF+d5IF0AXCfqTZuwDVSWP8NOlQkUbhcQ/jCyrnXJX5VOF9xghaZmyAWsys8RDnXDi6Zy8Ahb7GjeK04D2TSvMpKJgSgHrPea9sE00sgOYW5kWOsEEOQAkhqN2C750fswByfgeCsrtPHYA6RZ+8Q/EhInsYK+od0jxQND6MvBNJxdpC4DHREJR+ka8zAt3wNo0tEON9j1tVLQDdEqD4NDknlHH695pAKKsJnysV+bYYZFtECEHdFnzv1C7PyQHd4gBdjjy5VRZ7SN8tjgtQHQqDO8O3ci4nT78wcdQT7lTXJGwHuQ/0WfAzT3yAVjIkLRig8IZ1ABocuOG09Q7dYxvlwaEFhm45GIk3qC0RAeqASilr0Nvu+nTHwq2D9AYUoPb4ysktL3hnSG/nZr4gpo8iP8pJcJLHTyr2Am+Eu3PqJxegHxwOz0/Pvg8rgdaax8QE6JY3aGyNV39L1K5ygAYmFWZUUDT+PDQKUA+hw2Jea5iW0IYADbJgANTto3MDUBug7uRQ/4PSTX/3w3KoNsk5VMHTPz2oJvxFX40JzMitSNYZGhtT1wv8YXES5OGDPQ/0tWE1uwcPHgzv86gVgPIBWsuQKbdYftG3DPibIhkVQz88roE+UNcocPB0SjK9xGAN2+LaEqCkPP0oPWHP+NcKQJVyroXXKvcsgQDq0nUyyc59SASO77umVgMoWLMQJ7mYj1qQpn368XfXJGwHeUHlf9Kv8rj3HzLKjYsH0KoBKN5zPhsETU2DssH2RA6iCDju8qPw3lZk/vM5la47q5iHa1gog5qSX1a4CUjmB1dodvP+pQVvf3ePV16lczjTL/9Zn+YS5o8nK3cFwBLeghk+KgegQE5wmO5BMXa7+uZCSfKW2arwLPyTd188R6DP/R17FVCrcHQPDaC17NBXIxOgYFZzfnhR+eYhhV1+Hqi3EZvqpZb3aG1K0KExSn3NXQWA0jLNBWhvt+B9VNoE9ToKewuYCED159OSi5WZY2RgZspdGQAFQwNwrpeXLobQIClQape0zDEJ27Hj1ZhqAdS6EniW+NQ0aGukaVZmNHgvFuVUWDYebhshDxscbNU3D9bcl7cZQME+NwJA4214E4AqO7XbiPdYoqwsfIA6MFRz2pOy9nglgfhM1+IcgAJeAh/XAikIW5IGPqUb1TUJ27FfgFZqwTuuxPMEAIrkN+WDlpZnXaywbRcTgaqvtwEG6OGwPUGnsRBFb8JzCWofHr3ng4E2NEedkx2AhkctoWkQgFr07CyI+vtHgC7bnQ5VhfVrJZ2VB9AgN2jNPyxeocXHYfSK3sCwagD0yXuz3v33LcwDrcJP3/8oQfG5vdB2vG1RLwDdbD1QFKD+BhCgh+0BqsydvxlAveKBzZPoAFVLXQobAX4ICkanNkB1P6pT3edtJ0Mhk8vyznXMtqiyABqpSG68DB8cWJluLCnfn2mxAfrRt6yboIWJ9BUCUKiG0AGK5hrJptBo+KitAIrYEAscjAw4dU9orZ4XTGqmxRRd0Y/ilemUv3xP+ihS7DJ905rHCRzm5RDwcQjF74bFm8YJnuMHrxF/WjbZcWdkveR0Lc4EKNaUccPlyPHu70KiDNNhR7BtMQnbQQTosIyd0TNNAJRZeF7zJHw4As8X310agMImFeWVXTYSTaWbp70dflrf8MiGL523LmAbgHqNmN44Kw3QSD3yAlCkxLBw8/n830BOH6BqgeTw32M9bUmZfX6++OmCygNokKEBKLFIu0pht56dlpSpaxK2gwjQtw+He8+9eH/4/3Dv6/Ry47ogQCPNEzjc8wEayRrPpWYAuvVL5dLtpACgXrv94BB0BYpa+Jyz3gigQU7oPUr/ITYBqIEoWKSCN4+x58hIpYNy7RlD0AGij7sxsWZr2rTqAEWmc6SiSs8opQA/WfuXdBm5GpOwHeT3wj/zm+HfvxxYyn+fsS4c3ZOs+Ly7L3b3wnvoTa9ICFpmNNqrUJAXp3AXE0BaHw5Bv6f9vT5Fl8ysTDcBqBcDKnsbA6BjYGggih6HMW8OO5c+jd70bNjpzxHo3GTv/CKwZlR9gLo5Tk4iNuBtu2LVaakb1iaScZNJ2I6sxUTePnyxHx5KauBRztwKb92uqdsW3OkDNFoWZl9hAIps3wag1kfk8yLXR9C4kb/FUJSNUZOJndk2AIUyWoxx0+ZEoF1vgs8gHfjL5lyhEYkzNBGAjuNLjzvkCpQ3o3IB6uWpF5bNKFEnzGoZZlz0OsvZfTA+xflBA4uJkO84BStxDGAQcQL0uBczsGoAuglA3foGb17k+Aged4eG4isg1MrAzWkLgPp9G7MFcAiKLjQEZNuZFjyEt9AA64Oal/SMAXRO5NEqadwaAHXddHIXiKrUSFHh4+/bA3RovX/2Qq02PAegqbxhYo7dPEm7gCQeQNOH03/Rs23RJuVnll+68yW4UV3ZPsKmLWGTmTgMtY/1ctkAoD4/dTwHh6Dk6XD9ksn4N1quV9RiAARQk1jfGSe8ZsJRwCoAda7aySmlDj+DuuGXGhW7D3Rswk8rgbawoHKsqwMONaevsZdZO3kEBtFW0Zn3w2nKAlB0zwYARX6wEZssH+HTPtHpoKUItQ8LctgCoO63zvQSKWB/xnxiHS/CAajbXjf/mpIXbppQ1IpDjXknqL7j1lE8lQ9QJ9vTqXfuXGoeOWXMW6iH8tcDHXo/p67QBhZUjtxpATkdpBKvRpi/C9Dk4aCJdQPQTQDqfQWDqkWLj8Dmu3L3gcUVMNQ+Ijya7qPy8Nf5ukwJUnrhuSRA8eBvygHzic8Yj4weQDWNO2/B5HMTPn43RYpFVQBQ20+n0yr8hAqlZs4F6If3h/fBP3l14Ohr1YbhGQAl7QFCUaLHwjsRXN8APRxKVRZf4bvWB2h4CuBWLe0jOPzUh8UImo9Qr5ERmJSRUUap9nHON6uFM7Z3goypaypMm7sFfGgSY4UXOTjB5hIVj0Pzbi7H2KkHpZMuTxFATb6no3dq62krgA7rgZ7jzg8Oh3v3D2M0WkOlAI39ZC4pfHQmD/XSeQbZACUdDQCUVDL5mA0ACm5ATZp9hDXf9XExgPZuk5xgYiT8HOBQkFGW3MOU00M0vxm4CKA6XIwB1BlHUn73vg3Q4VO3TAr1cjlGz9y/HCQ/lQDUxz6ZhAAAIABJREFUdBCp49E+M3oOBaJmz38W/l+HhvuTYV3ly88DpfaA5h0bTecsEEM4GkB1QVWIHrI6QOHmG27T5CO0+3M5MEHQnDA0wU/6YiKFN6rf4PGiu/D9QlmreinTjk+V7jyiqXfO7FT6maMunMfUk2a85LaoywCqdGnHo7dxRW0G0L7/H2duPnnnwYOXq60IWghQ88hZsCeGTp2EZpuXiQ1Q0tFBSdkt+BRF1gcotCnWKXvCZi95+aUISkao3TUTJldq9SZ8wE//R7PzqBp55XmwceZfFynenH6QygJoN4enqgigDkJptbgIoKa7IfNmY2lDgNZXIUCd53kdYhIGicgcwyIH2vEQQGnl6sKTCFkboOiNjensowg+wxA0WtcoDLVva5D2awPUPmgmVJBialzr7zlvNtD0ixlgygayMAC1eFwAUAuhNDcVArSbS8m92TgilsAehX/+p2ST6CqNQJ12m6Xp0bWEyNcEAyj1YK+q0usChZ79BgAlb5x1OkX5mUnQNEJT/FwdoD4/oUyGLZ0xMBOg50O7tAVYELssx8QG6ILQ1QA63b+zRWY5O+rh5doEoJ+9UO3xTadwdA8RoPa2uakStKIoR6dTZl9Tv7IRI18iPEeTaIaUKr/ypviZ04jXNsScMe9BkkyxTKoEP7MshfwEA0H7ooIAheLW+Z/uLm7X0lUKZWutBsoG6Iy4lQA6O2jO3dxsRNM4Yt5sWU8i1Rb3vfCelErccXMqeghqUmZfU79Bh1Zy5X6jm7cuQAuAkuSnlSmRoH4bw2tvRDw284psfMH5upcOy2Nk13KpwRfvWT1SVs7jP91d0gakxiy81BlzATr2tNIImgdQ68xdgG4RgBLv6DpPItVWbYD2pqLEkuW0pReDsq+pV7PB4MS6a/LgOZqUkTZf5fyk5UolKMrQDh1KtEpaF6C2iXge+qd0tNV93U/wS2A1yad/oHe0u7ljSDMA7ZyXGzMAqqcVJJPlANS5hk6Yvg0/acXw1wN9ZoVO0LoAXWK+xDXOuC4mo/xr6oaggUlLvVHm8ek8rQrQ/MpL7NfMSh7NifIrmeGj7DM2B9jcgxJq1qpximP4U6CctE4A2mELsi5bMcIGAMUImuUj/Wh9XGSABhV/6niZbza6YSxtAdCP/+lweOq5l2Z99dJPIoGyeh2j1zgHVQFAs+xxAOqZYEUbhI5bSOsCNPcAIhCDELSQoDR+rgnQgJ9oDtZPqQ1Qa79XT5Z/Fvb5yaz0MYAqPY5fDaBLztF0RIBCcYMB6EYBKK0g/iCSrYs/Cw/J6Q1KEDQr18mg/Gvq1I6wq9P+nB9+9usCtDgAzciYCVDS3jUBag60ey6RrL0fYrzopfE+/NMpZQqCKolSCit2PYCmEZoEKF7th03zeqB0u5gSgPbzD7bT5xirzxnZzm3BCaB5JkHRg11v7DA0v7qsCtDcA8g0tHJmEDThrmXvegANL23kGprrSwNobwLQKECnTQSA9pFO0Jx+YvtXI+KwNEDxztQFoGSz2FofoCupKkD9GoZf4ixazYlLfhQtA5yAEzKwgKArAjTbmJwhoZKjgkxoAeiaAA1MiWWwhKAIQL32iR2F2mEffBxYsGaltaRJBYC6haNnTAIoXsi0oDLZLLYIZV09QP0AdNqGEjQr56VbJvea2rGm23QP619+ELomQHMPyEBh2IjPrnNkfq4HUIBqUYC6LZlY2YaaeoAdA2i070Atc/rMmlDww/DFMxXwEyY14fFS3PVAN5AAFFnDGLlOBfdK0Y/iUnww0A78fGcTdD2AlgagxGdaguMyKx2dn6sBFORnLAM6QLt5EVC79oAF2GWDWc6/2N1ybzABSq+gLID2I0CpRlVR+sSqAfQ9kkFE1QOoafD42+Ff52yCjgDNNMoUFLwwtoZZKwI0M73mJ/WhwODILIKm3GTvXQmgVo9MeDxycTvTFRQte36XnLK60EGAgmU7WWqAdrq+KbcRNCsDoNSUtEEktJzjKe9O4GsDgD750RfG13kcKj4VXxGgPXZ9wSuVC1DVl7UqdK+T0+ODV548gq4G0NIAdJqiQ8g/PLSieWp9gC59j27T2+yF/DA91I4B1ILhPOZjZaIb9V6fj/0FyHI2pLvr1NIR2ghAoQ43S8f4EqUrKFlx2QD94P5heh/S4XCv2mPx1QCKBaDTvnBPJiKUKmxVmBggao6zk5z5egDNTD9DEJzjCObPImhO5qsBdPrX5ZkBaA86odNdQdHC9W+uw0sAoH44GuY4/3bfDU/Tq04vbl8O0Iy6yQToib4Odi2lHvjiAvTD+/M6yr/81v1Dtcc66wG0j12QsM7kxlgDQItaFcGdkKBLDkHXAmjuiWp+nlte0+FJijp7MgmacJC3N8dH5POG+OkEoPMfP0XXkwA6s8/b6AHUdwMQI8y/3XfdVPiYR6fCR5foAA22YElpAEUPJ/YF1VSX2M9ezu7wtG65P3m1gffCu1JxgIZcymsr91O3TN4R83Gd/TBJOjjLMa24lsVxVdqAh5/zRrpVwsOJBE1Gt+7XNQCqdH8ifLTXlLeSDORCAaqsD/pn1+KlB1CoURVExNPyjtMacXOWVQGKXwsKQCP+3h6g8VVX+woT6a2os4G3croCK7SfJNXkSRRR2i1jzRim4LPPCEJLa1kUV1mdCCa7Mz/Deo9TFApBiXOgchrwqwAU46fyPsxfvN5NjA1Od2en/DauB1DIpZ6zp/fHdfMspjmw7aBheKKPgGYcKwKNHH8BgKb6rWouZ9fCe+EdzXhKJPJqdk4B/dCvnXmALujcgtLVhX6HkhIW1rIorjj8RCeJhxR1CymbREraXR+gMD+BAHRJrux9WPeeC9AuAKh+pbtlA2ycdvaET2UBdGDqHQegQVlo2gRA5yPbAahSqat/zREoIQCdkkW6kJIqvqbjGGgGPgfR0pZZFI33st3i0A/3US2CJn8l/Q3VAbqsPYcdjHRGTsfedUfkJcILU0bQ3QV9hDZAE1dparzf3Y01b1nlfSZzFxpfDFA87c4ASrjy/D7QZ8HPPFUC6PgPsXGsBbxWKZpD6TUdZ5F0qdwhS5OJeACFLjSPnwkfeU0AIJdkYbkN+JoAdesNahlootLhYNc9RqZ6acZOwYB+et3OYtlEuErj5KVJ0xiSPnaa01QCUP+oqBFUgCJ5bA7QdBIuQD84HJ7/9fjp4x8eqr3eowpAFRmgNkEhgMayiF7TyJF64V+Cdb6lqTRFteyAAzSX8lZ2s0Fxi7wmQJhNqrB8ftYAqFNDOjQRkslS3bqp//F4RFojVmSrghZ8P8+zUUFkilnc61Lv7pQy2am7cO3E/Mcf+pQVNICi983GAKVUe/Y80OGF8PcePHhw//y3VgBaCaDjP7n3PnRALI8EQNEjJ4DmWJbOU5tUkOvhgBK0hJ9ZAHWLyG/DF/CTA1DoxxU1AQhA/YMnkJ6OqvcQav+ij9+HVZSDq6GfaE9eJTXzU+d7t2zol1jYOaAEoAkr0gBV+vcA0tYAJaRhA/TJP+n77t5/oJuWUA2AZgSgwHHeNjx5IUBN1c1WEmh1AVqCTw986XpvnVN+CJrdAdpXAai7CTvUjqmwRs2As8ePTRKQ0b15ltMtolsOi5+E0mzSYLaPG4LbzntzLclHREfMigN0CYixbLYFKKniV3gW/sm7L54j0Of+rtJq9GPh6J4MgE7/1gEonkvsmkaqdSE8l2xj+wtqmQZeyKsK/CTVe3NSuQBNR17AxrqDSJHfSesDgE6T8PFxbsvrbtHgF2VCXtBsmQCKdSEseZhYeX5X3cOHD3vt9ofTNi9vIkDdYuKpKQCNhKCbApRW8a92NSZyv3p4YFYIGgcoeiQHoKmjEzExcOwCPJ9XycAGVgFAe/sWR3OCj0vkC23Nn09ctN9uwccNPT4ex8e7cY67k1R/nqcddX4RA/USvUEujheADgw9fxn/TmNZTsHEq4Z8AZUGaDQE3RagpFTXClDoZsw71N2GG5QCKHhoGZfcnHGTYseBQZDBlMurOvyk1vuFoGFe6YPw/eDmmgCNd9QsOSR+9Y7jHKO5eW07fgHo3NYGylCdv9VL4Xes2gCdPgz9q51bOWgAdS1JiApQ7GdvQ4ASa34FgH78owfDONLLv6YZRlEFgE7/VgMomk/kms6tEWoZWSoDqFpaks5mC3gOrwoxr3zoUeu96abzbYsdksgT3lwRoBETnLgvbuj8ZgN9fawhnTl/vRyoF2uq5eUcuHke5Sx+nlvx84exE1Q5Z5MP0GRyEkDxNvymAKUl4wP07WXw4Yu0IgliA5QRgGa24aMAxY7kBqDxM8MsshrJjgFOwGh95vGzAKAQQRNt+NLWdT2AxrxkteBTztQvJ1yukBkWnwGqPwcAjTcUvH1eAHreMBNUh58mPa3n2rEkpShA7ZsWPqMNAUqt+2yADvx86rmXXvx8TYLyATr/KSNATgiaBmhWQEtWPkDdZqH1BQZoYfNdhfzMqffzTWxtiQK0uHeyGkCjXnIAGi9Dvx7bSucMJxmY+kCMm+jvmPnZGYAqF6DmwpMAamecTk8DKBqCbgdQct2vsJzdM9NyTB+92s5yds6lyBZ4VDFAkdcqcBXJIrGuz/J13uABb/5WzE8eQKdywxAUTZzIDNtTEaCxfWDEDxt0co+Zv0yDSp1upAc/LymABtsDgLohqE4ympQw2cmeVl0oAI2EoBsClJqQ/yjnM3r6UkPL2bECUKQNj7UFY895I0fyW/B97BKDFoVlzre1z7vpa/FvD8DPzHof62CIpAOzQvfUAqi3K2xeT3/T3lx85Kad+0MN2/wy4wAF656aR941QPUXY+b0Ie0jYyuxvhABioWgmwGUXvuZAH3yqrOYyDOV5oIyAcoLQPNC0BhAsSNr8DMzukJ+FM5bA0DNBC2zCeJnbr1X7qAICFBCr2JBlJ6dj28D2LxOztIcDVosCpsK0xNIJscMgEIWd3YXKAjQqTgKQGGbUZEACvxMzNoOoOSUV7mcHS8AxQAK50YBaBj7ldnl5YLtACzCKjgEvNTEoYhFEI/7gnqv7Mk6YY4kesbdXA5Qt4kd9DD24d7Iu84tg4xFAZWXiaEORp0S4XMFtkIA7Q1BrWSKDlByqJIF0DDPrQCacYNeI0AVH6D0EBS9pnZtpGSfLTSXwKIIbiDeFRIUjmdHg7LrvcMcN0siPROXnxGBRvr9/Ob39Je05oHtI5Cgdo5EgCKtjs6axDSnCgA6pEuudGv18aaSzooB1Pfr5QCac3+ym/DWCkwfHJpowiM/iznYqgNQLLs6/MRDUN+iGHAA4AXTOInWoPwsqfd2q9fkqcj07BPNsNxOBfBLomlhAlBCEY6PsJ6BEKDRYAHkZ28Aag5dQlA7KbJCKW5WWjSAom34rQCakfYKB5HglnPOrZfThqcAFBlb4IoI0NhpH8Iho6UfM9OYMYtqAO2txwqNkRmXMOHlGgANSwBb8CR+ej4KfvzdrSHD4WAT3AYBdCGonfaY8ne8/xUSGaAKrLfbADTr/qwwjenpn4yffvXlNqYxYQHozFBS+RltePxdNmE7K5Z5gbB8Tl6qJD8tz1hxZKYtOD9TCyojm32C5vktkbgCQEN7wBa8CUCjPj35Vw3IGOgUotLc2kgG6CkRdbgdsyQRAYqFoCfzfuyoyPaAyjq8ykT6w4MHD4Y/TUykxwLQ6V+id+khaOplYK5RaDZFogCUwE89bV4trf1sgkb5Wbjkn3lLT1FEHN/NByhgN1jllh7Q+EmEL94D8opwM9GdYLaqZRKoDdDe2TSbtBwAm5zPzwyAgiHokQhQFkrzDuI/yvnO/fm+uff1nIKjYgAUqVFmK8mrSO2DDKIBFAtHWUJyivWmOXKAZ9e4AoAG+dkGRQEaWbBqtgnLN2kQLjZAIccCDQ0zIytxEsCbS/2c7U0EgILF2AB1j0QAqisGmFdgZ0oRgAI3jJf1uGw/SRyUZt6eFRYTmdcD/W4b64FapLS3Ov0raR9FaoxvEOm5H6fbvxpAsXEtuywqP93UmcSK8zO1YhUeguqrVQj0iCoAFEgFJOvcfggyQIE+IOhXGAUo4oEUQG2CnpyDgHgbjBKjygCoAjYd86dzZBM08+68tuXsEgHovCvtTnIIigIUO7IiP5G8nIqPHxy7o/OIpaIN+ARAe7TOGuNzAZr2MQOgfXhvY6nC3yT0LEIfOQFnEJAlAIo3u93HkKy0AUG9lgyE6NxogALQh3DuwxfGIBIVobm359UBVP/FAlC9N+EorAcpNIgIUIXtYSkK0MQ5Rm/oHGSpRACaXC8gFoKmTY0diIoJUNC1YQveqmeHbID6BAUDXKRaoVfeAqifOApQ4J7B3BATAaDLA6buaYxFcUbhiQTNvTsrAPSXry/68aXngVIC0Hl/3J/IXipA9eFWBY9nXCgwM2hZilDx+zkPoIn8YgC1/kUyzjWH9BvFAyjCTx9wCgIochaQj1yCQp1CKEDhQqIADQgKzSe2Cyx4q1caoNoEt4NgLpg3jYnWd5eZKRug/3rfVI3DpZ9EQuoT6JUEQrkAXcrw8qvKT/hOCRdGA5QK6ujISvIz/bgrMQSlWBPJzTGJmBeUI/JoO9jOD1rwOQB1rqFfaBiiYaZ4OXY+KHVqfwcYE1tMK6jLOEBdivfu+elSmfNA0wTNPyMuQD842Lo4QJcPUH9RkDrmUGobPgZQp5GzEkCB7PT0k+iB1QCabMBHAer9DfJ2rKFGxIQ0mXeiW53ARzP9AFRphk6y7hLYoNQaWsF8fBygkUsfDCFZyf2+UcwiXbkLGlNJgD5cbLCGkZaC2BPpUzbn35z8Rznvffe9RbXe6lEI0JwAdN6DOpTaho8C1C7cqwu1BAM0WbnTRCIii8BPwtNa+FXIsyaWl2sSKSs4T/jRIrCDEGjB5/3IWD/Awes43ULtj2BWOj8coHcuQfFHRNT0ApD8qkyNQCeC+g35Ck8ilTQ7o2IvJmI9C19PpQBdPlACUJ0S8RoxBI00vZYw1DKjNj+hDE+l/FS2Pve5zxGYReFnBKDAJzhFbo9CSgyAIs+2g/2kUACa109setP9E3PyR6OHMDscoF1HAei8VH4JPwkANQQ1w3BLQTUe5Yw2O/Ozq7kaUz1VWpEe+AKlhT2KcdU3CAaoycDuNFoBoFC3bDrMBe9k5epzo6wNYOk2P3MBSmh1ehEcfkapjHyTSKnAXGFX+C34zo2digGq8wC6HBGARj2g3JWYnAMWgM77EhFoSU0mAHQh6MPRg05BVZ6Fj7U683NjN+F3ANB0asilUWJYBuEA9TNSaKYchTkmV9FB+eml8gkaOorETxygyOewBHoISvQvpwkP3mhhAGpv1A7CHZV6s4EXb1qFeAYk+Kl7OiHT1Z3TiI/3gRYJBaidpyHoGOxaqSotJkK8tUniDiK93VITXgusWIkDIILCaT2DsOkndpNKx6IrBKCBQUod6/AzYFaI0elDip+kubJ4VKBAYxBRHbw2QO33afbG+hKAji7wgGx2BAZEHXBOFLTgMwCKt0SIIgHUjkHdnt9aqzEhjYiSrLgA/eyFaksw2YWje4gAtb+QSgQ8SusbhQEaNPpmU1YBqMshwkK40G0MValUR2lH4ycK0Mi3wDBaG57s39oA9Vrw+iXuPkBLH3c1NcjZ3jmVDLPNzSlowTsAtXfH54HCcnMORAOoQ1B7T7Xl7KATKbs32fNAP3rh8PRLWl+99ET6SbkB6JTOT4hVFiJAgayYP9+YXFSrpI+o/IxFfSM/x1dNTGNNUbTRAEoiaKSUMMuYSdSE2gb8G7Cp8wJQJkBNxfFKdsIzKEYFMlpax6Hx6q6zV7pLPIkEyEdzoCRA55/mhaDuGVVcDzQ8m7Jbkw3QH9q945eeBzrJbdlQy4QICid0DYIfwQP7CuEJhGxZwwfjx4SPyPxMQWus6p+bFSsx+rAB+t0zjgJQ+u/TqgCdx+n9APTgfXQNSkzQWwBjb3Y6CJ2OdzyfIAAFATokSDwLHyiMbQNRAWoQ6rzcpeaCyv4JFcY2/D7QFgEKf04eRruj3c0gQMNDpw3rAFQ/D6mrRBFAI0mxUjXWpqGmSIkIQJMbTEmK1IbPuNiVAerWHTwALQWoVZLzpfOsSnkgAdChTWE4mFiNydMSNMYISgBokN/0UudR+asxxeScVGnbsMZE+orr2OnC0T0ED5YFoP6BwHcwTwSgYN60t+Pka+5v0+bGfZTBz1Qj3u7/jFVAfKYCzYqpqicBmnMP5N6JCYJ6Aajf3rZN5wLULcndk/QA1AWKAFT1R703C59FALUC0OWD2xE6ATQ5Ppol+7xKM2bPA11jDKnOS+X8z5QjSd1yzmZwGTJklGGdLtCl3bOYFEub0YDvYwD1+Bl1NQxQ0iZTWvpNTTnOXRGgahmCB1rwha89gYtyKxqlhx3sAjVZdneD6ZqDR++HGdfDh9UAap2LIahOdqoLUMtnxXfmFU6kLw1A+8CNmFfTAAXz7lYaRPJmG2cDNGYVSi2fnzFfYwNtlG1mX6oNn+XbugB124KdwlvwfIC6S8u6AE0fGwfoMAzfLy85Pk01tgCf6DFEgM6FBkiuDtDlZi3O9/om0rM6Nmj9yg6tIIDCRw0N+DUAqryugZiPMvmJAjTkZ8TZIEChhDHvpELQvGtdG6D25yCocQ2HCZoBUGV9sis7oYddt4spAH14JD1vBESf+EGEJryyaqRP0ChAC4MTBYz5Zog/iFTtTXJ24egeCkCtz7kFuxUGrT4xgCq0Io8ArU7QqQLYWyI+yuUnRlDTJ+m8EgTJA+4nBhQ1JQXQyKGASVmpMwC63I1qLYBavFP2aVM62HUAigH03IYf/mrMppEMNt7xoxIAHQNe5yl7N+cT/pAdKVaufORoEraDCNAnr1Z8l5wpHN2TrGbMkTWfoFgqY1AAUKzWTWRdoRNHeYamI1A3g2gB4N0O8hM/NwigcEoCy5GKmHmtqwLUHotYbkefn8Zu8DxyZug4ADVfKE34HICekl6F+z4jByEAtVvwnfv0ppP56YTfkX4YkSFOVMNtwn/7xcPh3nMNTaRnBaC9BxT8984YFAIUz5nVWECzdA0i9IG6OSTKAO72ZUQnCGbhLKBuDjhlSTRMOBAwKS95CqD6r7nCaABaA6B2j+ECP4oPiACdHig69XG/wviMHpIGqDcgunQ6TDNTEYDOR9Rv3qXFX87Olt8h+ocfvPLo0Td+bm35/Tcfjfqzf4H3z4Xn26vFC0B7P4RN3uhBxUdbUmpeIrLEKCRDY5/TLRs/LI+fMEBBfqIeBwCKlVYaguZ6lQdQiKfLje8HoKEHofPImiNu97Kaz+lrCXeBAgAd3+w2WhS2pkEBmUGKA1T//KAERQAKhhEbaVWAzrScYDnpk1csgAL758Lz7Z3luD6RlpJFMgT1K36Ht+AVt78lyBCOtnN60wjGAK1+hJ+Yy4F+YtyiLFNoh0EmZaaPAVRp6W9uitBVbIA6kF4+kgEaJFwiwAWgo0n2rihAnbxiBqQAajZYoYEp5gSu1AiHERuJ/ShnTG8++tLP+0+//+hLv102/e7RX0T3z4WjWaYBCn3Mk9OlhaVZDPKeGEb73ZeWXZ2L7Gdkd8tm5EFI5d/tEX4i/goBGrEpx5R4sTHVBOj0qIy7K9KC70GCZj6laMee3vfYUQmAuh2QJ3cfCZ8JI6gARQh6OoX5u+FqfYImslwToJ+8MseZf/oPy7Y3H/11dP9cOJpnoppVCECdXNJteB+gOHJ1JemK7XKK8QvKByiV5e7dbvgJ5wls9OEQKzdqExb35l/q7GcCkZ+redg42BVrwVcBqHKwQ+wCVBOMsgC6VCwKPlM2wAB1+nQtW5ethqBBAd6NUB2gKafWAegT8GVI78/R5vsGmn/4vgVLYL8uHC0pCVDrczxpPJtkowAGKP6iGFMf2A/EKwCftqXkhwKJhjh3e5yfsNcDgEbNIptCOwRWFYBOF6ID7nsVByhA0Nx1Mqw+TzNulT5I/eMdkDAO0HknAZ/JSxEFaNCmMsH8EoSGTYFI06CGUvlVAOi7Xxk6Pz/7dy/7Q/BvPvrb8a/VbP/9N7/03//m0aP/+HNkvy48395JtaJ5++cvXpJT8SMNdKuN1zEMw/DZ270K1HyoRdp3e4KfoMOCKD1uV9KSIAiJ5QeLD1Bv0MhNF/CzOkCtUXfT+5o8RnX/2OUCVO8l4DNpQgKgfmaLM63VmRSQwt4UNyBXSZ+yAfrkh9Po0WcvHJ52h5CWaPOTV5ZOTj2GNKAT2P/HWqcyHY9H87kwDzcnO0cvBbgNT77sOD5+zDELtQgtGsuJnHa626fDND8jGSeLLt89F+4kyTvvUrmlPH5srsMRsMbaBrsr5UWKQUshy9+UJ47H//q/H4F0JqfH/xWonGG+Q+GPoc0JA35hFB4GFzNV1eNRE9S+xYFKXLc2MHKjAvS1w+Hp/+3+H/3syX8+HJ5xYlAIoL979Oivftv/zx88Ou9aBaA1Tn0+HruqeAFRulmmPS6z7RjD5ynvJyOHnxYE0vxMm5EqObp/BqidZhN+2j+AIz0eW1+dZEdv21oABapRDYA+hipnmH4iaG75UYBCVVITdPiwENTZl7SVJUZmRIB+cDh8fV5S5J377vuRLEAuE5V0t+cwlgTtn1X8XvgaQ0huZngMP+2wml4qMhXPH7YtsifR6l56FUi55ZStG5yp9juStddPnLQtaUk46p0rRhPemi0flk9rwfdhz2jBWsFBLUq6Qqm7f4Qqqeny76y6eQr3L98HHwQzntNXAmzCR3sg9AyxZSjJ6rhADkhaQRYhL24T/rXhWfh5Taa3D8/au6AIVOt3j77028j+YoBCH0sVDgdApdkAJXSZTkKHmiK2EPos6QDNLX++3Sn8BDL3+ok5xmkD0p3UcTH7QOMATQ0hme1mRz5Aw9FIPkDnBe1mk9Ccp8roG0C4FPkA7Q1Ae3sUKXLacd5NAAAgAElEQVRfJq2gilK32KsxDeuBzgD98L4zkT4G0CHoXBegsXREQWMEzv7RIGvdbvyq+gOMeQSl4XMxNW/FKpIOjpLZe99dgKZLS4eghAaCjl3gc60GUGg6rr1pRYCGM9lWA6j/iq+p/rohKKVOFQB0Km7y8/SOJIWHn1Q7aKJkVGU90Bmg/uKg+Cj7xMzqo/Dcdh2YHxWgih6AZhGMSs/ForwFV4jK4Wdwtic8liEdHxrSLz9vsVxCGZPSVmA2KedKJ1rwNkDdcWvPmSUA9c8+DdAOnMVEAahyvszn6U6ATZscA2gMicPcgGH3aX48P35H1AIo6S5ZE6B6fqeZ5/mH79vMDPcvhefb66qSD+cfu8heC6B9xOVQJxLNgAx8Lr0K6UypGS7K4Wdwug5AKaUR2vDTaWR50RazD9TqQVBBorAFP/rMm/lTBaCZbWgMoGZTd2esP4EpZgdMHzoFJcAVBWjM7k5ZACWOBTBFy4bdhB8GjmZyfuANwwNPGr05BZsTSFd4EmlWrSjeDTbC3b2p+NFoNbziJCzm0VNblPO0Fl05/PQJ6A60UQ6nhKDkXyEn41KAegTRrVcvAA22+fxECVoEUDD+jR0BTwOlANT/BdGfqGVPKgToGIOeYT06yVkvFE5dp/1JSsVfUPlZDdAzTJ1BpAGTf+496/7JK8M0pk//ZtwE7NeF59vrqF4vSDTKWX4UF36iAAUyJinX3uGfuI/K+EkcPzKF2N/cgTbS8SlDgFKyxALoiKIuvO2BzrzF2nD6eQ2A5hF06gKNA7QzUSX2/Jgdf3fLJ5LNEECTLfhxdzf8Zg1OUl165ega9z/xjLgA/fD+4U9+MwL0oy8f/BfMfWqttvTJK2Oc+f68GNPP/f1u4fn22qrXjZwIAi2ARsuFqiyFoQXm9hlTvbJyzuGnd8bOQFv+4a6yQI6KCdB5BRG/BT8nBFrw0AM8zpkUAtQf3UkcwQKoRU21bNM/GjSbYwBNmD6A83ii3RdZdRx+QpWaBftJpOHF8A/u33vu8+e/wds9Pv3BmY/fGOPLGaD9p//p0aM//avfBvvdwvPttVWPn4keFzW3KhKVAMqhpo1etriPyqg8Z5xHLbscC6D04lC1AdDpTgaWEUkGoAtBeQAFSktSqBvfeRQDqD2KhCxCaNch3UCj1qpigA77zwA9xpuEJjXNHHSFKXoO/Gfh//W+bt3VezsSD6AVA9DebqZAO5eBwT6rBV/bSCdb2EelQa3JN/MI89GZqcAurxWAWg15O4GCAAo/RG4TtBSgjl1JgCJjSA5AlxOAl8FWLkBVdPqzLxZA9XvhSaMHBGMwej4M1yzBVWExkY9/9OBcC556/qfEIgliArSeIWN2kQeHlJoAOn9BU0Eb+ZaBZYE+4tGzCPfWIc5MBerh6J4qbfgqAJ1HhzXJgHSzqQs2XYRWAajtqbUBOuHLrkhTQEi+rihAk5VzLPY4N/f4IWgEnoPo9X3VBZWLVe298FXkPN/m7xv7teMAhS/4eiFo0HfFg+eYR5kls5yZCtlHB2oIoMCiTGEAagHUe1OvRVAGQIMgGD2ECdA57PZj3owFGgGA0gLQyddHb43naGpcCXo+DN55EtE1ArSiIXOO3TwPDdhlFsnOasH3K4agzrT1CvQshX0I0DplNgXQXsdkCz/DFrzTbrcJWgWg4Cf4EC5A+6nf1wGo2gygQxPeszaSHNmehufGAH3yq9dff/29Sm/jXApH91AeU6xpyZIpAqLhRxFquxEsWi0ENT2ONeDZF1u6HFYEUDx1jTY8G6Bz9Kk/wxNDJ0MdfmIEZQDU8hR+pYbyuu6OCdA+qFHjTwcboKRWuepzAAqlgWlJOxYRD6AffUuPIN17GVyUvlAsgFa0w8pVaSB5O44nlQAoHplWs87NdnlcoxKii7NZ7kRyy4tWbDsAXb7pJgoQgPoAtQPSSgAlhKBTmSSA6m+QRUFTTBHHxSfhAE0fC0WgIfxMcmAbAZ7YoZg4AJ3WUl70b+uFoZXeC19RymqtOUVMA4N9jJOZkSlX6liTnj0H9B5Aa5V7EYACbXgnCFQhSmB+9vZT8VUA2tMB2iFplo0JgCr/OaAp9KZWNg5Aez0Kv9xqKAGXfD1R6Jl5XzIA+uTV4do/9dJZL46B6DPVCMoBaC0b/Hyt5ppNp9NxSZA4ENhVzTpHx4rw7Fmc13eiNVe2RsEV2vA1AOrOhwRaKDZA3bweBjOZCt6JBHxJAlSlAKrOjI0BNFjDbn4ii2Z1CFByC94C6GxglILgeabhOR5IsGURA6CvnS/887rh/tG3zt+eDdIUqsprjSvLnbyx3CrJOY4Ri1YytgAOEbGMnA6eAZp/NLajFYD6u3CA+pk9dJJwAbp4Cr1YMzaQFjwZoCp4kHJpmpGsRgFKOtoFaBKFeAgaL2UjgH543506//YheJSzWAyAVrIAytr70Z/uFjNFJx+gK1lbGaD8o62HDfIOjoegDLNqAxRMcogA1E/EBaiCtlqKd4F6naAxgCrgNoiW7IgPUKuwFEHhEDRZSF49LQfoa37E+Vq9Z5GqvNa4unxGjpXnmJjjGG2drGNuVYAyTRwPtx42yDwa2c5vw1cAqOkMtPvHrRQUflYCaHImSBZAdcMBSOX18yrTJUCqKNUASiMoKVfeQcUAHXpAnVcgDa9HqtULWg7QOuVjuQfZj90y0ToQrxvNA5SNeB2lF2W0XgjKB6i1HjvYAWoDNJbtTFAuQOMUe1gToPa0KevQcoCS++w1QHWZ+SFoUrnHFAP0sxf8Fvu5TX9pgK4agML5n+Yxb2wycQKgaxhcEaAV7NMALT0YFDsErQlQZJLwYUuAJkLQCTToNNAcgNqJnREgSl0JAJoTgA4A7dyyqoeg2RWeA1B3/XlgS7GKAVqneDz/0L9z/16HLPOa+nFtHaAVshgXXCkk8WohaI0mfKc/wJPGaPw0BGVZkwhB6wB0eQrJLSzZAWvEBOjp6BoQJWjOkiCLsg+4KoCuHYBCPFwmicNhSMqktgFaxaHjgiulGbUM0PlW7pAfyQWgiXyLAAr0JiHbR+kWvEoD1AzDAwB1musBONPVBQQofdbdSc/QWw5Bu0GnjbnVLr/CXxdA65QeLcJ3sbcifThKmcqvqnmTSbUyqmTcGaDxwY0CE7hteBZAR4NHgCqFr9WlAZrMuORcoO54ePugh5kAncf+gjTKYabh5vIJtW4WAlAkdaAzQL02PEJQvS2zBhdU+KsC6BbyCaoniVu1STnfUvnVNW80qVZGlWxTxxOSE2VWM+LB7QFqd7zpyDOyWOwhF6DphIAtvfkNSgIUXU65zwbokMA6xAcnWnG4AD15nQg9WIcemm15SCyo8ALQXCEAdZPoupa+fiuEoLV8VM205XFXVw8fPkwTFOxe7C8J0MlgvUIX6iMyP4sI6gNUsyIJUOyiGghGAWp/AYyJhcGD+ABdek6WjX4VcqtVTh0uqfAC0Gy5bkYeU8T6RIHs6lmmTaqTTT20L4+72nroKGoHZAmzDc8F6BlH4ygS7qQMKNKDVd+W3o7AUIDOKRQPoP5CdvYeh6CRiuMDNK8LdByN1ABFCOrXqoxaXFThOQANdSMAtR2tX1UNJ6QAtDpB6/iool3AFJ2HvqKW4IPcxSYVHDMZYSwe734scRbg8wka9AFqe3CAKjZA0SphR6PRkAAGKJ7ekwGoc5SpQkCdygAoOaVtErZDAIrKqUiljyla2XHt8VUJoFVyGRUANMBnnKAgQi8dgQ5ISgagxJwXgJIJ6o9Cz/bAVTELoP0w12n8evIS0AAaDRpqADRow8eX+SRnX3YTlz+J9O2XQn310k8ibSO7jlQAaG2CVvFRTas8gHr1nEJQ4L7kteEZAFUWsXAn5Vm3AJRIUJCfI0EjAO3+scOvKzCKlANQi6BdxG4AoE6uiSbb9F74sJ81+ntMrMiFFZ69Iv0qahyg9nWeHlNk0aZFgFalugPQsJ4T+NkDCAVaQI7iJhWcBtRsRlUCUDpBg2B4sQe4cPO+rkNXA+2JAE3ZM36MveADBKhTRLTiTQDtvBJ77K2ngW0xFVZ4AWiRzHUufs7bzoxvkK2oj4Afa6jqVTXJAihYGA0b/t2VAmgUXyyAEhCaGR9ri6kEdbtj/Y5ZT9OeqQVfDFBvCAk/OvqCDw+gTgt+vr6xu2GsSFMbXkUI6huHZwidQZ4EoGWqCdDaISgLoFP1q8v0BaBYmEBuuNp2pQEaqbM8gCqVImhu/4JFULoplgWLOesBNFYnnLn09FF4C5jm5zEFUKgNH/1Jo1Tm4govAC2U9vj4nDcXoHUJygRo0TPEcYNOQNFFOTm38SUB6r2fOFAuQDMJOprilq8xGQcoYRxoBOjw/eTsjtdyvVOpPlafQ4Dq+dLOHH1MM0A7KFnkalAAmk6CmITtEIDGpS94+UIZdmZsc2yxAVpIN9ygU1BwaVbOjcwgaFE9cu/x2MlktuAHg7IIahF82Tb/9AV16eE2ALXDRzQpBNAgc7yg6ZcYBmjkDUnpG7T8FhaAlmq+5nUAWrfFHNtJA2gm4RIHnn1Uj89ZBEVzYQF0jpoi55PNz7OPcgh6NiAsegRoOISj+QkA1JsBP3/CAJqyqE9XZDACDfJGc9EA7Vzbk0olZdx/AtBiTVewCkArj9nEdlIBmkG51GEsNgMiPZ+QYBgToNM/OEJLAJoxFG/46aR82ENcmQHad3cdANCQoBBA08+EJNrus1yAjvzsgJzjAMVC0Jh12Hbq84KoBKDlGh0/PJ67J4DGlc85Anfr4rOn3M796gCd/sdOqwigdIKagsNdfgj6EAeoAw/zoZvWmcoFqCJU4wCg8DK6WFnlAPXTKkf0jEKTsB0C0LQG10eWasvKiZ/HIpaP8kgX4DMFUI5plijVPgoxDkAXVliBoHtm+V2gExuoBMX5eaYLCFAFA9SKWM2eYUnl4fvJSph2t4o9l6XlAbTDFgNEcloA2uFpEOtMzpXIqU3CdghACaoG0KohKM9Hiow6gJ4JgLIM88xMVv/PjVKweAC1W6z+2Q1/8vnpADRJ0Jg/vdcOpwBqhn8qADSRxAPoQE/0TTjgVj0fLrsTtPevP/3IhASgLJ1r2bEOQCsSlOWjZXw3lRDGZwygHLNASxOKEZQNULPBPfXxTylAqZOZYg71H6VcAHp35wWJSg/8eOM/uhPUBWjqBOylUdHkFkDVuBY1lhDebgBK6nF1M6xMTm0StiMHoO9p/bqSVTsCKLLWZX5OVXIZxfHRXMOSuEtj00/LMApRgp9RjDEAqgyHjLe0G8Z/C1rwmg0kgkZ97gV1c9rztrt/VL0HUP3XxVEAUBp5rNAXTb/g8403RuDi2YJ7NEBVPkDXEhugT75lzRq5jdWYbNUD6AatCooZlESEqNM16EJXLcIxFkDd4fg+cEgBPxcfEQgad7rHriUAHQEKBKD6NPgANUlSAH3jLJXoNQV2LTebyu8ELVaiFC5A3VXtbg+g2Grr+WoCoJQ6mUnP/hoB6mzofZ9wAJoeSEq43YWXDkDPNvvTQJVyPvoAPePKyjFdL+xn5VE0Lvh8461kvt6uIfFys80TBbYgaKIMLkDfPhye+trrWj++jeXsbFWDQ73aUG5R2obc4HMy6FJXDSdZmUV61CUAqOsYFkBTBE06XgFWQWNIDkCdVxzNXYyZAO09gEKHaH5Og0jxbL3xrrHnWgN0uzZ8qgwmQJ+8eng226a0bhGg9ULQAouIXewl9OwLfFSrw3QVgDpzYsw+n5/FAI0TNO166zouiQGAKhemKg7QpP02gp3RfUcTQPUofCJfG8hT0mXEdpmrurpSRTAB+tkL976XaxJBtwnQWtUhxyJv3CWmouBzMijPR1l5x4WijANQBQLUOKiEnwBAQYISfG91MVgBKABQ96AOAygpAFV2k9oZ3Xc0BaAxgDpYN+VPH82cQXU1AK3V7ekWju65YoBWC0FJFmWQc1YpPnN9lJ19RGsAFGkG94vlXIBGCEpxvokFl2sFANS/6BZBF4AuICQAdC65t/5iAJ3ngcL5OqGwcvB5CYAmi2A34SUCrZZVrfoQtcif60NWKT37TB+VFYEJgxkHoP4GI4ufHIBGhuJnIKaMVHbqeRDenQYaXHq1TEPSjwcd6dVEQQAFKrQPUDAruzPBTeQAFOtnrapkAfxBpC/mWUTSbQK0VgiKW1REzlnF+CyLQNsFqH0rQH4s4qfnozhB012HyiFoHwIUqAHmqcrpUaR+XOk2NtvdKtPJ1ADUP9C04FGAOl0B3pOeZuEJtU0Imi6AP43p3tfzbKLoVgFapz4gFhWCU4sRGRb0ga5NUBZATX5rATQykBSud+1dVSdMnjIY0HnnRHNQ43mZh7QAdNza+Q+HhvI7PxHLJoDqRznxd4SMhkI/9NbSZ3MImrKMqXT+xQDVb+X8yuFw77kbeyunrapTdNYDKJOePKrl+qgmQesD1M5xNYDGhuL9QsPuzGAOUE8B6LLfAehA0FTVMXGtl7d/oAlA34q9fW6Oe4MY0wao2gCghPyLAXrD74V3VBegVSpEYBGbnkwVAnRNgrIBejAboPLYAI0QlALQoKfWBSgy/j1vnlvOM0CnJTtj9cfLFh9l600A+lYsy5HaQAbW+3PmJ0HXrdX+2QASgDJVd5J4fYCWdnnWVP480B0AdMwTBWh21hhAAYICAAXa8B7F3AeRoAoxj3n3DkB15YlVIifi9DN3j7IBirXgvW56D6BbtuHDEDiUrMbEVNMAbYGefYmPVido4VUbfHmIE7QWQHGChl2gAED7GEDBOqFBCQI0hlA/4PQAan97wwEomJmNbP9sHYCu34b3I3lIAlCmKgO0RoXQS9Y0Ac9BBT6qSNDaAHUQGji4sAUP+cgiKGCE/VV5zPJSAQAFLNCHKR+gJgFYodxYN4Cac4jhZwSgVnFe/j5AV67fhACUPQ/029a40Ycv/hsZRGKqFkDboWfPAehqBGUB1EYoDND8nE/AojQLQWEjrK9Qq1k5322AwjXDijQXgHq1CKxVfvQb6aJVaYACR5rjJ4BaEfGqdZwU4dZ8EqneY0k3DFBShVBRHZuiZ1/mo4oEXQGgbjs+VRhFEECRcJYKULvDcxxDCvaA2U7j7j0E0B7tMLC+hYS1AGoWBMW6QP3CegvR03vht2rDK0o8UxOgH94XgLJFqBBxfE4ArWsUU0U+WjcE5QIURWhVgIIEDSM8L6b0h1+IALWGZzRAwR9i9+c5xVcnjQtQ0IiwLOv4YTGReYbTiPmu019WCBloeC4HKDQM/4w04blKX7NkVbkSH60agvIBChO0tAsUASiUH9jHGAtBF4BGW/A250baYgB1ERqEmwAYTcG5ANWjWnPa4wTQDgFoXYKSAlBOBPpBCNC/zLYSKRzdcyVwiChx0Qj15Fp8xCKoc2hIoQoABRFays8eebcWwGigBR8PQUOAwiZYAL0zAMXSBiM88w4IjEti80qkfyYCdG6pTx+PR7vMkaLOedNypJZL6h9gAPTJf3vppRfvm8eQXvraT7KtxApH91wLHHDFrxrld/ZafMTpBnUPXQmgAELLAYq83TUkKAjQHgSoYZwF0AgU9ae7YeWm/hSN6rzuyWUz+ODnHEc6AMUyBo7VUfbxZNuplHc7VA1Bif2rspwdU/VXW49cNloz5Wp8VE5Qf92TgGuVAOoTtLgFj78eOyAoHaA9AlDEBLOjuxswGAdoj4zhwE/Oz4lNC/6fM2CndFfoST8cpe31DKwI0CXsTajmNKZ6um2Axn70WT+Kl1KxjwrXf3poa9yyGkB75SC0mJ84QAOC+sPeYcemM/5eBNBxGOmYqG8KbK53cNucClDQf7qLc65I5pQ9EyqGoNQBfplIz9QK7/tJdDsldUU+KiLow4dpgpZaFPb5OQRdA6B+WOsDNNxsjZHP38zqHHgL3j5sHJx5nFqICRkwgvMfuytNCx4EKBrAnzO9CwA6Qtk1oSJAuTcbYTWmc/Cp12SS1ZgqCr509FHGa/JRPkEfBhq2rghQux3PAyiJoHALHgOoAgCKmeAAVE0ARVvxY655AO16G6BgIpyg3d1dDwJ0nRCUf7MRFhP5o5/5c5lkHmgVweOY5LpxVT56mIlQK32MoFUB6iO0JGNrpbZQdr44QFWwbWmJTlM755EcrBQboEN9Oyq1LMsZdl2MnYTgdoS4gwXlAB3XZ1oAarotVgpBBaBbaRWAgtWSfPh1+SiPoE7qSBu+MkAdghZlTAHoAbAAxqa10QA0EYD6AO2t5d9Dik7fw2oJjyzpA0wXKAxQjKAzKo/LMg+msE7Z2yqFoPYr9uIppQ+UqVXeeR7Wypx6cW0+oiM0SIkOxFcD6LJlTYDaBIXHkPyPXoIFoPFh9eWTBqhTkANRpV/NroJM0Da/Aeg///Nb0RMFTeu649H62us2/GKeexIcgT9GoASgTG0A0Kzws79CH1EJCqTbDqB9BYAmCRpY4DIO2BoANGKDPQyvxlH4IIGG6DD+beXvZAGex3TUGymAwsNwOhJ+fDRtd7196puwyB45QarsfoF1Afrk/1pjFpMA1G8sZR19fT6iERRMZL44t+YKAOWMwbsrtUFaCJoJ0HkuqJ7FlAdQONE4e6jTkPFb9j1cyLRDr8aUAqj/9P+c4fGx016fW/Z3zh1SIwR1+LkqQD974XDv5V9nmEbTrQO0dy5gbo24Rh+lEZpMsT5AnffNZSoF0IUsMYAqYOv0fR6ET03s1J8iALXy84vtcYDOieYFld/IAqgp4PS4cy0dTml67tRKjllN1hxcOyVh4gP0rHvP/5RqG00CUHMr5P+iXqWPUnwkBKn2rbkSQMtFBejBb51ApkQAGrOBDFC3qzOI/8BB+/HP/FK5N/75DQygIUGt3M4ANW338e/ZzmF+E3wWpcoIQPl9oL/61v3xhJ//CdU8gm4eoOZF2wW14Up9FCUkpY2/A4ASCIoGoASAJqqTB9CIj1wYB6EvDtDppUjnAJQMUNvm09HpN5hWZgpm+3MviJ1f8urWGET6+EefH8/5C99NG0eTAFT/kpdUhmv1EY5I4iiTdWsWWwQMrJRm5chZKxgW2Lp1vyloc9/T+OkA9PwlClBgpKrHAaoWgA4EfSMG0NiDV2eAdsvWqS9WhU/fc6+Ind0mAD3ryf/55eGkZR5oNQULJdB1tT5i8nMdgFYiKAGgAEG9A7AQdAZoVxWgLkGdv6iPRoC+MbTgqQB1TD6dFoBag2LB0/e8C+L2gG4E0DNC331BAFpR6XgB1RX7CGIkfZqoRdA9AzS2tB0cgo4A7ZIVymqUD4lxH4Vz8tG2u2vXOIJ0DkBjAHW6WlQAUDXZpz+oPnyilHdFuvC0IqoD0F99Z2rEy0vl6qm8Flyzj1j83AFA8wgKTWOHbKoPUL+Q+DT2ZfP8GNJ/IQD0YGWrdTqN8+Z1231OEC5ByrkieQFoBYB+/KOx9X546uV6Q/ECUI6as2hNH+Xw0yJomwBN5hXpH3Q2BH2jM0HjuTMA2kdnmXoA/S9UgPoAOztJr29in2nYhi+/JCovAGVPpP/Ph2keU7Xxo6lwdM9NwaFQzVm0oo+y8KnvzYe7BWisg3DaADWlF4AmMucA1BqYP/iTYZUH0HMTHp/G1NtTXj2Lz066m6d92gANz4wBUIV9gVVhHui9v6s+k14AylFzFm0QgVKTa4LuFaDKIShqDARQZKVj4Oh+mseE+gjF9AJQbKhrBugwkBQJVg1AvZ3Di6PmaZ/GBtWHsXXxNfEC0E0AWrf1PhWO7rkpOBSqOYtWByg9+QzQh3sHKNRDaG2CAZo0wh2GP6aSBUNYI32AkS4LoPMg/Bmg4KlOaecsghTDWzmtiUz6LzDeWnhNkAcTImL3geou0C/8fcWn4gWgHDVn0ao+yuKnIWhpccBNVYWg7ut+IsU7AMXsuwhA59Gc2FSBN2ad41D4VCcWwgBV41s59bx5B6BhG77smlwAoIN+9Z37U0/oj9MFkiQA5ag5i9b1UR4LNUBLCbouQJN5nffbYyyofS4Ixh5QKkAPRrFkQPljUzo81CRXIzun5+HBHwvdjwoVr+a3ci4ANWcKjI8VXROfn1sBtB+mgX7rIPNA21BzFjXlo2UcqVArteHpAMUWZtIJgBHyAaB3XTTjAyjEBgygg5AW/PQg1LSUyARQAFDLSD5EYTW/E8lrwyukDV9wUZT7Jk5SFtUm0v9yeCZeANqCmrOoKR9Nt2a5Rc0AFBhkMTnYu8ZZkx0IUBibCYJag/V+J2gf8nOm4sS4t2yAhgRVeiNEYdVrgHZO4cpdD9TOK1PKdxvhmCoA/Wh+GP7eyzKRvgE1Z1FbPoq2TtNaG6DxzJxGdh5Aw8RJfOYDFEJfZ/jmAtQ/W8t0Nxt9/OSksA0PAjT7qpQEoHyAPnn3K9O5Pv139UaRBKAcNWdRWz5qGqCJzOa9GqBwEtWHAO0AgBL4CfoJAyjIz355HeggD6B+p+OyzZ+tNSdzAJpow2dfFuWt9rcJQPVL5WQifUNqzqLGfMQjaAsAxd9eqdOEAA0Gqkn8hN/R7puz7PBb8NP4v0li+PlWkJk92ORNNtBpNECVlx4MQXMb8ar3AtCtAHrva5VngQpAeWrOosZ8VBugVQiaB9AYQYOHKmGAxpE5TAxCinCztnd0YeDoTd+fVmOyAGrHjsrko+yRMlPIPNcL6gRl/7CVBaBsgH5lhRd6CEB5as6i1nzEIujqAI1l5gMU76N0Ijt1N7wTyU6DYNOkOUNq2hHlkvO5WwBqht093xh+vmWyMH2ZuuhusdBtnM9OQjpBeSGoF4BuBNCVJADlqDmLWvPRoe4ofFWAxjMzOyMEBQHq8gVroZtEZ0idZhr6Q+UwQMdnhPQRC9BUCNC3XIAGvbbuA03O8QtAHeSqGiFoGIAKQDdRa3DoxUcU7R6gkUa814Yf29EuQDF+IgDFyWJRzzT6D1aXpHc2hinBR2sAACAASURBVJ/2YiKTwdY3azjKOV5PVSC24b08wSjVOpGSAFQAytVVwWElXZWPKoz3QsoGaJSgAUCdrkiUnx5AlRVQgia44z6xp5BmvQEC1BkDWowHbNROgtrwkQujQgUp+6IhJAEoW1cFh5V0XT6qMeUwlAXQSG5O9IcS1Ado5wD0gPPTA+jSWewDzj9gjnkhfrqpDT/d5ezUsoyxogHUb8OH3a3Tnt413oepfRolQ0jNAvQkErWq4/Hob6ibPWnX8ag5CCQ7WkmPx8ePh//mrwf0MKeAx4+Hr0vK41GftWve8G3etRiEnsvx+P/8YpFb8GLfnNURzG7JaLTOZD+dL+C449EYDu1YNGyCHVEqiUBpuq7oah1dm4+CILFCCHo6wiEeWg7YYjb7nAj0zqxXFAk/+yACtecrzBGbH1OaSezJAPT87a234AjUHWzvpwWdw/yWimS9EcmcL9Qy71KzGqzhLtTwmBqNQNE9zd2K1waHNXR1PmoEoChB3eZpZwAaa767BUwAVXZiqPvQEApuwbtG9RhAlfcw0dTlgANUaYDanaDAZaGs4acTe/YQJQBl6urgsIKuz0c+RqoA1IRTlGKxfkK903Radsv7PFL8DADqMSwk0pD1vA/IOehWRACqdF+lKQY8OwugnV3AEh27mY4do8iZ+ioMQAWgXF0fHOrr+nwUNmS5Op0sfODFehZoHgaBofXWtW55IVIKnwBAlXdAZ4jZG9L1PQjQcMYTBlAn/RLp4gAN2/DBKBLUMo+o9HpWAeh7WrWeShKActScRVfoI793j5mdDdBIbuFsooOFUGdU2YRz02oeZpZm7Bksu+GvIIA6zXinzEQAqiIAtc5cOeDz8zQVCWzDew6YP+Fni5i6LUA/+pZpF8h6oE2oOYuu0UdhdMXTANBkGz4EqP1Mpj03xwdo1yWb714BXTc9eO434u3hJKvZnAhA588IQO1kywn0BIAibXgb7LRr4zf+KcdMJmE7iADVyzEJQNtRcxZdpY8UQDOGzj5KAhRk9sGRhmi3tK27CaAUfDpF4AAFB6/jAah2FgjQMFBdPni52gAN2/COdVaW8TMGEuVcTi5A3z4cnv7a61o/lgWVG1BzFl2nj+oSdAToEk5hJUKfD54mgHR6+ZAsfoYADQbilw9OCBgH6HIYDFDcEhSgYRt+bv77+KRdm/IubSZAn7x6eJZeGFkCUI6as+hKfeR0uXEzG3yUCkGBkG745BN0BM4I0LHtPvyl8hMHaLBck3cExE8VfIIAipytHYLqbSFATRt+6l3ofHziBThJYMMJYq8Heu979MLIEoBy1JxF1+qjiwLU+hQS9NyWHyb6TO/itAibNAMAqB2CogBNBKD6EwBQL0u1yCb/vOloA9Rpw88RqDNHwKSlnzVykrjYAK3V7ekWju5p7la8VjjU1HX5CMJYLYC6zWJayTbA/EB03EvHJwRQh6AZAAWHcSCA+tnFAGq9ql4FnaAdNm8+enWAJv+GAH3yqkSgl7YgUHMWXZePoIZ0HYBa4VS65GAbyNBMfiYAChkGDpf3IeJHhQDFPQcMI5nHteBOUKD9Pu3EiwiPybuW/EGkL+YUR5QAlKPmLLouHwGzc+oBNJodMCrjJQUhmsFPCKAWQUG7rCAXysnhEwDQhC0uQE82QL02/PgVASjUrg9nEuCJcXEBeg5Bv55THk0CUI6as+jKfARMu6kE0D4K0EgL3hKLn4UABfkJRdMBQGOOC0NQ62mDsA3fRfJD+gk8WwGDk+I24b/94uFw77mXZn1VpjE1oOYsujIfOXdYLYJqgEZyI3e+AvjsiGZAADUEzQLo/Mc5JARo0hgXoHYU7rbhp+5PrA3vNBr8iaLO18wryR5Eci6TTKRvQc1ZdGU+UgBBKwG0Vw4U/HKDj2i45POzI5ph8+loNk65YGZFA1AnrQ/QuNvs8alxwzjSZgx0fm06sMAlsbYK6CcdvgtAL6Urg8MqujYfAaO2dQGKdzZ6H2NDMNbDm/RV3awMu8dmoyYofEgsAHXT/v/tnY3PHMWd58cJRnnwrmCFhizg47QQLbf2hsTZu4DgtJdIcJGcTaRNYCWDOZKZeGEXEidHP37BPvy4//Kbfq/3t191dXXX9yPLz/TMdPVvft39maqu6hpJoJZghCooO9ZruOo5fOfwOlUUpe1k4vafZwseszFR2Zoc5mBrOZLGvcQUqF4Dch+S/myvumE9Q83RY1rMqQ3/MfOkINCKRXWRVVOTFgRqy5pKoFIVtN+UsfIuttOFV5hQfHckBEpka3KYg83lSB74EkugtaNAlXGwb63Yi4R0gfKj6SuVQOXRCbKyeIFakyZWQdmxXl0c42cdd4H3nhjWY7boAQRKZHNymIHN5UiqgnqfdxKTQLWXVN0F2hcyCpT7TU4zSoHWbEe8+Gknf44cNP4UBeoWjSDQehLo1IafLmP67gn+aq33fowg0G9+fWW3u3TlzViTgdYQKI3sItpejqQqqLuhNPA/a6wSsnwJ1NwonZq4MQSq/InjFqaJPQr0UKmj4wTqICu2G2knjPVi2/DhvehMaSFrRxDonfFSdbwh9RAohewi2l6O5I5cqkGF34U3ClTo4paDGf0aS6DTVVC5INUV0IPG7rxAXaLhqqDDheLp1Wr6w63iDnNFIKAFTxdo48/vPv/G68/FNCgESiG7iLaXI3kojPtVRjXC78JbBMoFIV9QGB90lbPqlntwGoGeFKwQZYPYhVQNM0CpVMQK1MlVbBVUHKowteG57wc/B1ZcWv2vxFAF+uBsd/mj9tHDd3bR7ouHQClkF9EGcySdaR6VPCXMXMHuAu3/VLw/mCpVHIF2TrQKdLoEetBVejmBuoXDVkH5nrapDX9QfwK3DXAbSy7Qm7vLw91HEecGhUApZBfRBnOkqILS+pFYgarbkqJAmYa81P/NvEAVqNjbLrxf6kLqN6yuhDICdUyXUqDMr9A1j/jav5cF+dZ/wJdgzNmYHpxdxq2cGZBdRFvMkeLCYySB6qqgskCnJenyaP9C34oPFGhb8Tz0vwmiroKy880xMVTDtoVPMfnzE8dscVVQ4UJx34YXv7v8BMouBVyHId+JxNx8FG9yUAiUQnYRbTFHohuCLqAxcAKtjAKtOIFy7xZb8zVBoNXHoz0bbt06yOOVNIPo2W4s7nMwAvUKSBToWAVt3Clm3mM/iPk6OK85AIES2aIcYrPJHKkESjAomyOrQLv/2Suhmu6kergo6RwHI9AjV9zhljC7MStQQUNCbXR6bfKnn0BHgwphjpVd9UdwLJwN1nnVHnITfndtXLi3QxM+B7KLaJM5UlZBww0qC1S6RsA/4rvg+2a/FFLtK1BmO3yODuMvyzNP9l4TesHFTq3piUmg7M8aW+Jhq6DT01PhckXSeTcovnBcV+1BJxKRTcohMtvMkdpWoaXxAlVUQQWB8hXQVihK50YTaHu5UTToWCcV38iXOCp0qoDqBConkK2CCheK6+5HnxQidEMh3tQCfXC2e+bD9tGXr2EYUx5kF9E2c6QQqPfpN8HlyCjQsad7WqgVlxunYqIItGuYCwKd2vTMG1UDmIbopgqoVqCKSxdMFZR5egxLWstDoNKS71dglIH0uytXrjR/MJA+C7KLaJs5ki84UhAEysyNUQ3XMZled0agwzoHlXq6Px59SAaBtq/wGhuvgDLb1gyI7RQ6VUC1TXhNFVQWaJeGw0G+eOHaEFCu5bkf6bdyfn7Wf7aIv+0BgVLILqKN5mg+gdZV/6OUDMOyugWvvBUqukCFKujUgOcEqi24qhpzdv7UC1TzPaA26PBDxkH7Qr1SaoHWF1+8fqqBPv+zSB1I7ca1r2R3Km5UDlHZaI7ECgypMPZWztaW4v2QTJc6Vz+d1lJF2P4fSaBSFVQlUNMdWY1AG3l+8geDQGWBadrwdSfQKligmiaE347EdHZENiqHqGw1R1EFehSqm8prrHVf5zoIFS95LA+zTgyBVnqBchdsLZdb//DJJ51DTQL1qYIOHVRhAuWXxs/gsvIABEpkq3KIyVZzFLMKehwEOhamcUL7lr49f+h1VWl6sGIKdNjMpLGdLFDbrFRt+/2TPxgF6lMFrSaB+l8E1UnXa0cGC/TiveY3OE//s+BXOXMgu4g2m6N4Aq2OR35ZbJMzAh2FyVwnVQcQWaC1VqDj64quLBZ+QmVrBPwziipoNV688K+C6jsBffZksEC/vdr8hBx+VG6zcojIZnMUTaBVdTwXn9HUb5kKX1Xxd5wnEChj0MFnnEAtSXASqO5zjJoZnz9UY6JCBMovMYsQaDo2K4eIbDZH/FkXLtBTOWKO3AQqriOV2/5PFyjb6S8IlKn9qi/EsjgKVP4cTBueUWgnUKZSLsZr3IheuR67EtdAiWxWDhHZbo7iCLQ5l6UcaQTa1zqHh8I6yvAOt7wiG0s9l59jqqBcBZTr0DHgJlBtVVpQ6NiED2nDGyqgPvsSAiWyXTnEY7s5EsYZEQpxF6i2Aqqpkvr8oAdXiEKgtUGgmpEAHOECbT/JkTdoVTMGpQrUEoEO6mQi7zH9Rg9e/2t0ImVAdhFtOEcRBNrJQSFQrkOGEag0iJ55lyK86ALl29JDMAfZYjKuAlV+NbQXipltV2MDXiVQSySWKqfzzsR0dkQ2LIdobDhH9CpoX4KcI3EY/fiA6UKS15GXIwiUGSMpVEFZgfKXSpU4ClRdBe0vFMu1UMVAJuvOsPnWdWfGFOiDMwg0B7KLaMs5ogp0OHVVAj0oBWq01FwC5R/K/qy5SZG023MWqKoKOvS0cZdCK8ag5hKE4mzvnl2gQgd8C+YDzYHsItpyjogCHU9kRY5UAmX9qRRoJS36dcLbBVqpBNregWStgDoLVFkFHQVacQ15XRveXL61we+4Nwk10HuyQK9JbwoDAqWQXURbzhFz7gUIdFpbLVCpbLYFry5RKD+2QLkq6PhS/+NEtgqoh0DlJvVhHKogdGQF3Izk0t53250EgV78yxtvvH526fnxPqS//9Bliy5AoBSyi2jTOaIIlDmPVQJlqqBSDVQnCNkMdIEKw10rlUDtam9wFqiiCsoIVOjJ0hjUVLr8PeMQgoqY10DjAYFSyC6iTeeI7WAJXlWdI41AzRubRaDS64M/u6VeX0No+rI9BCpVQQ+H6XfhxXb8FIa+AH3pmi8jp/0ZcxhTPCBQCtlFtO0cBQuUO2/VAj0IG5nGNukFKlrEcxy9m0Br3p+Vi9prH4HKI7JYgVb1EMR0KVTYtKkN79jad9mhGEhPZNtyiMO2cxRaBeXPW2WORoEqKqDabYm2IwtUumAoXBEd7gSyV0C9BCqUwzThmRiEMU3SxVqHsl1TqSaaQL+yb8sdCJRCdhFtPEfBAmWXNAI98O91qeZRBTqWfS4si6/3j4eQ+tFSxiR4CFQakXVgJgxgfsCdU6jbsFz5mq72jdYo6QK9+PVf/Vs7qOmFj6xbcwUCpZBdRBvPUZhAhTercyQJ9GDuQpIKnkegnKWGpWFuUlPRXgIV672cQKcXdQY1CZQr1xSDNUqyQO+dtXMwNaNCL8UaxQSBksguoo3naDwHfQQqnrjOArVvaW6B1pykxoUuVnMOfAQqVaWZOf8q7kXeoNMa2kublfKx8q22IKkCfXDWD5//j3fP8LPGeZBdRFvPUYBApRNXmFC5Z2wW994Uls3h9O/0vBFJEqjpNkdm/qW+x8u8LT+Bit1ho0ArcdoSjUGt5TrcMm8JkirQm7tnhpb7xTu7Zy1bcwUCpZBdRFvPkUv/s3oNJqCjauWKr9Y5VUA5iUURqPyG8aLFgX04vlm7QS+BilXpUaBdpxX7zsOBGdBku6LiUElVx6CCPA6UqXXiXvg8yC6izefIV6CKE1ct0PrAaGn6yUubQLkqVnyBss12rjLa+abSe8lToEJVcfjdE7mz6mRv2aDa4Z2V9MgpBiWYjYnI5uUQgc3nyLMKqjpxNQKtBIFq1+fXYjblfQnUSaB8QPXQmO9HNUWqgXKfo/m/E6hquNQpEJVBlYX6+NO6T1EDJbJ5OURg+znyEqjyxDUJlLkE6rYZtupGFagy2nHkEvtM88+oT3+BCtcze4GKLzYLh3GSJss1aVsF1RP6NdBnlY9pQKAUsoto+znyEaj6xNUK9MAOFHLoQupWYwogC1T5lqqu2TulOoFWldmfvgIdP0dfapukqQbJvrEJRTSoyf2R/EkW6L3d7oU/to+++SVmY8qD7CLafo7GGT6c3yoFdK5pcB7kkZY+V+7mEWjfVueugNZWfQYIlK9LtkmqxBe7hSYWsRFvEGgkf9LHgd48BXzpypUrZ6e/sSqgECiJ7CIqIEfOAtWduOdHdV9P08/Nt+CdTv00AhWa2Ldu2ULzFWgtCpSfJJULaBLozvCFJtx3SoYs0ItfDVFf+m9xQqohUBrZRVRAjobz0u19qoCOTHuYX+EwCtS5msu0c/0FOuqq374mrIoZjNl1kt+y9vcHCrSaBFofpDj7xwfZoLoBBPH8GeNe+IsvXj/VQJ//54jTMkGgFLKLqIQcuQlUf+Kenx8OqhebYUiH7pHLzebTesNfskB17xEroC5b8hco95GFGqhg0Ab+MqjqHoDKtBf8wWxMREqQA5UScuRUBTWcuefnbWNdtc4oUNcupJqJZEaBcp3klfAromq8BVpLAuWtyQWkMqgUdlx/QqBUSpADlSJy5CBQ05nbuEHZspQF6hhP/8d7HL2bQLnXWnmq/c8TIFA2rV1PG7MZ8SFj0Ep4a/+mKq4/owj04queL/4O40AzILuIisiR8oSV36EN6LxWn9xNE75zw0HdKtVubVifIlDj5sbX2qsPto/f4C/Qmq0yngTaP8W8OgbTvcZdBhXDD/Cn5e1kgT58l5lOCgPpcyC7iMrIkU2g5jO3E6iie2Oo2fUtZPfTf+x/oQnU+rZ6Gqw6i0CZRns/WNZQBa2FjiRJoJH9SRYo/+PGlyHQDMguojJyZDGI5cztBSoLr7FTawK/CujU/zK7QLurBE52DxAoe9VzmLKK1aYUzmBQxbB+p+u0/MZtb6cK9M5ud+n55rc5Xz/bXfoHr9gMQKAUsouojBwpKjzCqyYGgcrvPBz6lqdfBXTof/FvwfsJtGJ+0ngegR4kgXLdV9OjwZejQcXLst7+tM+oTBToxTvNbKCn/681Lr0cayQTBEohu4gKyZFBoPZ6YJMjpUGrcIH2LX/3NdgV2xzZL4FW3IhQW3ghAq3HT1BNk6Yy3mTiGSqdk0G5oanhE1PpiTOZyJ3di3VzUxJu5cyB7CIqJEd62zi0oxmB8m/v7uXsa1d+7fFq7HrygxWo5W1DT03FraklTKCHYYPHabcpqqD94tQTvzsZ8xZT6ZzBn5Gms7vX3sV5D5OJZEF2EZWSI0P907ouK1DOoG2zcxKoZzxB99y4CXQsnal5zyLQ8RfkOIHqDNo+M3UkNbeX9u84qO/1Mm3Z/pZYAm1a799ejdWGh0ApZBdRKTnSyMrJYee8ripepkEV0No6u5x2vXadc1sLfrqsUHEr6gm6BjrNw88IVNmIn2DqoIfhKoO/P+cX6MU7bRO+mwkUEyrnQXYRFZMj5RnnpjBBoONaY8+IfwWU7VfxXa8Nyby94dZTdhvzCHQckcUdSOaNjpdBT1XQNsRD8I/rGaHPB9pc/ewuhWJC5TzILqJicqQ65RwNdi72eTPVu0Gg/jI8zCfQivl9EcVYIjWhAu27q/gDyVgFHQ3aXgVt/XkQO+Wt23WAKtAHZ83vwV+803j0ZrRueAiUQvqILBWdYnKkyILrOSsJlL3AOPbW+AbU9eB7YxdoNfVO8V1e0QXaffquaEGg5nrvaNDqUHG4btjpXTHmAz3VO+/tdpfOdm1tNAYQKIW0ETkcmOXkSEqC8/kqC3SSJitSz3hIAtVtsN3bSn/OItB6/PTCgTQZVLXidBl0qMCOsbvkxPWLT/eC873w/9403C9utjciYRxoDhgi8vsOtsOUZyh2XTmiIKTAI9cqgdb9nTNCV7dPPH6t1mm7bUjaPjG2OpxEoP3tSOKBZO78Hw0qDGBwqok6f/HpXvCYTOQ/T968+PzKlTejzQgKgVLQRhTSkNEjlgOBivikWSnQ4S7JcIGSroFqLulOnVq19BlnE2ilEujU+69cdRCoogPOdhI4d4sFC7Tvf//mj+byw4BAKagjYo8YskSVBWjLW02OnHDOm1eCz9WTx3XzHFVVmApD97JOoPwxND4lr6rDX6BD8Qe1QI1VUNagijyYToKKeY8xvmCBdmOW4o1c4jeufSW7U3ElcpCPlXCJ6lbUFraSHDnifhHNJ7dqgY7N5bBdNfS/hKxX10f5kiwTxPRdrFhVR4hAu7/tIE75QBoNql57mphJ84tTmsxqvh0UEATa1EAh0FXIQXf6+UvUuILuhVXkyAOnpHkKTydQp146fQzeqzDr8T+0LEQwKuaQRKBVezuS4kAaX1euzY2n125B15ZyyTmhCb+7/Nuvvrz6nQ+/mojVnodAKcjtHPPVHo8qleWdmhdXkCNfrDnz9Z1eoI08DpSmeOh6R86XYgNmfN5nk94CrSaX1eoDyakRvzPvL/FF5rKrjfBOpDs7GQykzwFhsJxbbckRe0GqgDLPURDWU9KvOINA266kQ1gXkvc6zHZHgerraPJkT9EFOoWgEaiuEd9FPTTiLfUI7sWx3u8QYLhAL34Jgda5y8Gj6UeW51CKIqCscxSOPiv+6upzJF2n7v4aWqDG+PzXYdY8Tg1ZuTtpeN/Ba6OhAm0C0AhU1Yhnj9nJoK4KHfzpFCBlGNPFV7/517NL/+s3E7/FnUgZMEbkbs94lCRQ7RdUQNo1AmW2ExKd/zrMmufDllUfsP8rez2qQHmnaQ6k4T3DGsL3PWNQJ4VWXlOwxJmNKTYQKIU+oiX0WStPoHxzREfZvA3Ie5YC1bY8xrgOfhv1F2j/p70WrDuQxgsdB1Vbacca1EGhHq22mj4b03s/iDZ6nt249pXsTsVM5eB3FMREsdlMcxQLIddhibcKNCgu/5WYDR+1x9BUAT1oX1MSLNDuf92BNApUOWMdb1CbQrkpmO3EuBMpPhAoBUPNIQXylrPMUUzYfAcmXiXQiirQkECmVcVxoMzLw9+D52bDBDo25LUC7SqouuOeF6j5K66dtMkpto5YAr34j9/81mOzFiBQCscF7dkgbT3DHEWPaDh3Q1N/Pl54YcskhkRb1XBLcP9AWeGLKNBqEGi/eNSFVE2T/qkOftGgeoUOMze5RNdCF+jD/9lMRv/aKb5nfi6+9vSD6/v9W5+xT/3ln/b7l/qnntzYt3z/d+LG/eNdiuzkUFX6mkOyEPgAssvRLMeR79UznhUJdKoXK+tqUQXKl6gVKHvIqXaCKFBt31j7leCzG8kCvdOOXbqpHMXUC5L146edMl/6RbPw6DoEGpvTrj/a3zV7ENxibjmqZ5wPNFRa8QVKaYaYBTo8UP9GRnSBTh/kqD+0uV/f1HQkCWUrOv+GT+SuUKpA77XafHC2u/ynh1fF+UBv71/5rH78/v6Vr4dn7u9f+mndPNU68/7+Vc3G/eNdirzk0O73DCLij768ctSSXUQKgRIvxFDWNgl0Kld3f7m+XD+BCi1444HExyK2BaQ2/Pim7lG/eKgUL5qhCrSbhf7errkx/p4wIeij660mn9zo6psnnr6/f7tun2r/3t7/WLNx/3iXIis5dDs9g4j4gy+rHHVkF5FSoKQSZxPo8EDX22LYsK9Au/8dBKqqTrLNepVA+357ZuiSLGFrjNRhTN2kdjf7X+Xk2/B3+wrm3dGTT270rfVWnU/fH80qbtw/3qXISA7DDs8hIu7YyyhHA9lFNOaIvZhHKpG0uv6L2FYBnUGg4xPaA6nrvpOenI5CcTD9wKDQ9lYvsQQXhUYZSP/t1fYX4UWB3u6qm6qWeivQJzde+T8/2e9/+Jn4KgQawri3s4iocjnua9JlQwpZ5IglukBpeTUIdHigHe4TV6BOB5I2kmFtbk4RrrtvEKlyxju7QqMI9MHZ7lotCXSsYD66Pl0E7eha9UMfUu/ZdqM958CT44mlY+Bwiud4zC7upRnTQUzMTGtPL3xsf4vE7ydcYjiaS3PY4nCAHTuBKo+27i3Hjz9WFROcRp8m/J32Eqh4DdQg0K5xf3+//9HX9f/7YD+25CHQUDL0kD2i/sjNLfBlYQQap5y4a4/PHxMI1FKaHJSylPYo6wRq3lzUk8i5E+nZpvu9MafYC88IVBindL9T5nCNVO5LQhPeD76hkUNEDWNMujkgKqmNloxccjQiN+GXvASqbcKzY0C1G9C/4tWEl48O473wpoLYae0MWwsYxxthGFPDtfri3V1XDx3R1kDvX3/pbW55L9ZQIVAvhN2eQUQdQ1TqaRzHoCHQms1RnLREGAOlFOj4uuGGxzgCHb5emad0AnUpyyJQ9oqotTg2JN0L7gPpTzzbdiRdusa9ohPo3b3Q+S7VUCFQH6RdvnhEA2OvlhQRP8YEAlUIdMFRoLVWoJPcTeHFEmjtNh7O6aNWTC+S8vWpNPYtxkprG5LuBfdbOd/7wc9Of779Ly98JLyi7oX/VPSn4hopBOqOfEQsHdHEMK7qXHyWi3mJNnw+OepRCJRUXoTVj5XEYRz8k0yg7FMagVqLapD64bUhsy/bGv6zzsY0jP+8y1zjfHp7/3Jf3RxG1SuGOUGgrqi+UDPKURcdlyPL0ZuIjHLUsQqBdqPOu6GTQdv2FWjlIFDXTyrMTM9pVDcC1HrldFaByncitXd3fj09bsU5ipTZuLbMjA/8JVA2SHLKURsfkyNlEwoCzVKgco6GO4NsZWvf4CHQSjE4Xi1Q20Z7VDMyqauj46tzCvTivTd+8KfmfxZ+duWTGV8W7oW/y/YXPbreDGN6/BOpDwkCdUNzKGeVoyZEpodZGfECbfisctQgCpR8CXQGOVXqlwAAH+dJREFUgToPmogjULkg44/K2QJTy1Djz5rtuzeVGizQb682s4ic/mcRpmN6zMzG9Oj6qR46zF93oql83u0nY5JuRYJAXdDt+bxyNAlUe6guUAXNK0e1UEuvySkhZ7RSTErjPOgslkDFjRl/1li+FVPAbkOxaHsFdF6B1o8/OPnxrbZ+2Qr0/p4TaP24mRz0R2L9EwJ1Qf+Nm1mO+l9i0OsTAq1XIVCV0jy37i5QVQtedbJxYxYiC9ShBY+f9KCylEANOsosR1UzkbhJn0u04TPL0ToESt9RPgJVbE01Hq7/UzFLGhx06L8CBEpkGYEadZRbjqqj9UdGIFBBoAtfAlUJtG0jE4v1FajwpEKg/Zs1vek8GQqU+1XOB6//NX4XPg3GIzm7HB2tPzICgUoCJZVGz6ck0Bj+jC7QoVur4p7QESZQ83uizMakWiABgZoxH8grzFHyNnzWObL3h9iYQ6AxdpKzQCulr2WB1mJrzC5QZ4M6vT2mQB+cQaBJsBzIa8wRBJq5QNubkKiF+ghU9SHEA0lxsSNiFXRmgQod8C2X0YRPgO1AXmOOIFBOoItfApUEGsWfXgJV3WMnClTxUU1hegnUrb5KqIHekwV6TXpTGBCoCduBvMYcpW7DZ50jqj9jfB0JAtVOP++Hq0A1ozqlSRVUx41VoI4GnV2gF//yxhuvn116frwP6e8/dIvMDgRqwHp2rDJHEOj0OD+Bmiav88FdoMqvVFGgyjfFqoK6vTfmNdB4QKB67CfHKnMEgTILVF/FFmil/gV4f7wEKj/NH0i6Kx2GT+8hUMfaasxhTPGAQLU4nBurzFHiNnzeOSI2mKPksqqYu/MP9PJaHAWquy9TFqhmdV3BHm34NAKdCQhUi8Opsc4cQaATxApflFROAjXOPu+Hs0DVXwJHoZquvTVYW7KvQK1viyHQi696vvg7DGOaGZdzY505gkAnshIo+Yosg49AFc8fj8yC3p/WKqg9UOe6KlmgD99leuExDnRmnA7ldeYobRs+7xzlJFDymCoW9ya8apsVK1DjZQ5txM5t+FQC5UeDXoZAZ8XtSF5pjiDQAWKbOY7wOoG2Jou3Y9wEqu0dYprwlsvE1Cqos2ipAr2z2116vhnM9PrZ7tI/2LfnBgSqxPHEWGmOINABYp0vTiJbgVYO02z6EE2gtq8YSxXUGmgqgV6809x9dPr/WuPSWDciLSDQ4GMkqUDd3pa1HPQkbcNnnaNsBOoyy6YX7gJVPj0K1D6uSpcERzM6XysljwNtfwv+zu7F0/8313snUvgRm1CgrjFmLQcDEGhPLgLt/ZlaoPrxndNPGxysmyJVQd2HO8UZSH+v+WX4/v8YpBYooZ2STqDOEWYtBwMQaAex2yZSTf5U3+sKirlXKAKtBoFWTteIzVVQy8rpBdq03r+9utbJRAhftMkE6h5fznIwkbINn3OO1DMRORMpi41AIxbX4SxQ5dP18NswTlLXBO7iRo/x9uRroG0TvpvIbrXzgVK+adMJ1PmdOcvBCATakotA+/ZylNJ63ASqHuHZC9S9W8toUOOa6QRa32yvfnaXQlc7H2iX6LADL5VAPaLLWQ5GINCWPARaV8eYpXW4CFTTQK86gXY/Nue2NaJAnbZBFeiDs90LHzXd8C82Ml1nE344RoKOlUQC9YktZzkYSdiGzzhHyt+j9CCqQCPvETeBaqcIOT/3So76ffbqpUcFlH4n0s32/qN7u92ls11bG41BUoFOaQ45WtII1CuyjOVgAQKt+ySEZyLat1An0DhlDTgJVNmCbz/W+dFvXJXJoIbVkgq0/vem4X5xs70RaZXjQCvlQ1cSCdTnzRnLwQIEWo9X5ENTES2HjUBj7xAHgWpb8HX764S1V1AUgbptIcZkIv958ubF51euvBltZruUAmV3R8DxkkSgfnFlLAcL6drwGecoK4FGKmvARaDaLqThXniPoJRF2SqYPhVQTGfH7w7/oy+FQD2jylgONiDQ4UskC4FG3x1uAlU92/a9H7thTB4bNBhUuw4E6oOQYe9DpvnN85nPet/y85WDFQh0SEFoJuIdjY1AIxU14iBQ9SwhrT+rbhiT1xZVbzcbknvVmk6iQL/q/jx898qVH3xk3pIP6QQq5cd3/7QCndehvmXnKwcrydrw+eaILNA48TSHdvydYReo+i73/hxrBeq5Se8q6CjQyuXMpgj04t1+AtA73SZj9cEnFajDM6bV21aFU6aD8S44XznYgUBJg+oiCzRWUSMuAtW04LthTP6fT18F1azQveh6ThME+vBqP4Ny8/vG330upkGTCVSRIZ89VLHzG8zk0IDLsjNEQQICtTPkqCpcoMoWfNX3LDUC9d6mIiOmNvxuFKhT6eECvXhnt3vmZ92D75ya75/Hm5A+oUBVz3kMM6v4AdAzKDRkYEDsGKh4CTSNQbPN0fj5wxIRMX/V0f4eX1wEqgql75lvBtL7b9SrClp5dSFRBHpvGPd5etBOY3dzfQPp1bvD/UaHSpBDfIcGje2PGUAMfEYqQKC18MCLmOmbIUcOTXjVBxiqpSeBBmxUWwVVvNPXnwSB3uy9Wd/pa5734o2kTyRQ3dHmtJd6VQpyiN2UDykpWzm4ULhAKwhU5tatW92DY9hVBTkpCk3220om0FPDvZ2IqZ+Uvm5vi1/ZZCK6veFyFA6alOUQ06Fht+dH2XRE/ASaxKC55oj59EGJ2KJAD4M/uR+V80FnUHGj0gt2ggX67dVel6cHL/LP0EkjUP3BZj8Mx/NcKYdYDg0rIlc5uAGB9gRd7lu9QGWqqV8p+K4VB4GyzycV6KnieY1/hk4igRpeshyH04Gq26cxHBq4eq5ycAMC7QkTaKx46lwEyt4bHy5QMS86US4g0Dt9U351TXjjwWY+EBkzGvZpRZVo4Jq5ysGNNG34THNUQaAC3BkUft+0xqDi27z9GeEa6DgL6Mo6kczHmtmuzKvmfTpdyPELzh6DgUzl4ErZAp2eyVugqs4e+9HuLVC+OMLEE2JMiwt0GLV0qok+yz0RgyQCtbysf507QBz2aaBFg0+GTOXgCgTa45+IuLV3U47M/owp0Nq5tmIriEOtSm9/EgR6arE3VVC2Bd8/opNAoNZDTfsG/vjwmG3dz6Lh50KmcnAlSRs+0xyRBRotntqYo9B95CvQ1p+zCFTpSv8KKOVWzmYO5Stnu74C+sX4KAIpBBr6DuHY8b2+p7eo67e4lUzl4Ey5Aq1WItDgo9NToBVfAaUJ1KENn1agzb2cJ55pOo5ODfn+URTmF6jDEaC7TYl/2n+fWlo/ZH9mKgd3ShYo+1S2Ag0/Ov0E2m4nkkCd2vD+/qRNZ/fFfx2moW8E+kK0CennF6jTEaB6k3TsBO7TqMYUyFMO7qRow+eZI6JAIydOlyPCZrwE2vtzJoEqbBlQAY01ofLFe2/+0Wu7ZuYXqNu7pLfJx06y34V3J7uIfHMEgXb45iFy3vQjnIOL9BFot52gDgd9cQxZCTQycwvU9RAQ36c4diBQOxConW6y9RUIlFTN9RDo4M9YArW34UP8WapAw96oOnYgUDv+Ap3doFnmSP6+9isjhUBp+8ZdoP12CD22mhInINBQ3I8BfmCzaj0I1I53jiDQlgwFSvxucxbo6M94ArWOpQ/xZ5EC9TkGpvdq+nkgUDsQqJ0IAo1dcVfkiLoJV4EO2yEPeeEL5ZeFGmdQBbRMgXq9uRr+qleDQO0ECHRug+aYI+lTp7p1TYOcI/J+cRRoNZ11fEREgRqroBCoI76HZb+SZi0I1E7IWNk54mDIMUeqQR8+RcwuUPr3mptAK12thXiyGQUa5s8iBer5drkzkAUCtQOB2lmBQCO0C5wEOm0n0qDrqWB+mXMmBOpIwPBk06EDgdoJultrjkAmcswRUaDRc8bnKMq9Hi4CZf0ZW6CGKmiYP8sTaMBRYDx0IFA7ATkqUKCKo8xToBHDaeByFOdeOQeBMtuJfteKQaCBFdACBRqyjmElCNQOBGpH+Yu9+Qg0UvXWLlB2Q4kEuhMeelGaQOMfZdmdijnKwXuVudvwGeZIPfWCewlzCjTW7nARqGGjdIFqq6CB/ixPoMT1JSBQOyE5gkAbPLIQ/ytnylG0sj0nE5Eiil1/GrUZWgEtTaDxT0sI1A4Eauf8SBZoxGhaxhzFc7P/ZCJ8RPMIdAeBOjLDWQmB2gkT6KwGzS9HR9XnzUKgEXeFn0DliOgCVVdBg/1ZmkBJayuBQO0E5QgCrfMQaMyvsqUFqmvDQ6BOzHFOQqB2IFA7aoG6Z2GGGvt5/II9BDrP1GeaNnywPwsTKGVlDRConUCBzmnQ/HJEFmjEYDrOu3JjFuwlUEVEEQRqMGhIgSUJdJYzEgK1E5ajwgSqjmhpgUb+GnMX6FxzR0KgwcxzQkKgdiBQO3kKNHYzwEegqohiCJQvmOjPogQavqoBCNROqEBnNOhacuSagzmSpZhgj8ryAjVUQYOKK0egM52OEKid4F8ujRwHw2py5JiEWfpH41vZWaDzzV6uFWhYccUIdK6zEQK1A4HayVCgyqH9NDwEqno2jkA1bfiw4koR6GztQQjUTrBA5zPoanK0qECjF+kqUM2uj3Ky6aqgYaUVItD5zkUI1E5ojiBQ1xzMcnzPkCN3gaojmlGggaWVIdAZ6zIQqB0I1A5VoDFj6VlOoLP+AJmmDR9YWgkCjTMXrAYI1E64QOf73pur4FC0OXLKweYEqokoSkjKKmhoYQUIdFZ/QqAOBOcIAoVAuYhmEWhN8GcBAp17Wp/sTsUVycEGBFqkQGf+CdyoRti8QGef2jy7U3FFcrAxY9/fTOUGQxLoPHlaUKC6iOKEBIG6M//PO2Z3Kq5IDlYgUEeBRo2lZymBzv4b4hCoM7P7EwJ1AAK1o8+RQw62JlBtRLEEGi9f2xbo/P6EQB2gCHSu+x/mKTYcCHRkdoHGzNemBZrAnxCoA4QcQaAOKZjpOF9IoIZPA4E6EkWg8w5fGoBA7UCgdmgCjRtLz2IC1UcUTaDRMrZdgabxJwTqAARqBwLtMZ220U42CNRKGn1CoC5QcrQeOdAw5MiagvXkyEmghojiCTRWyrYq0FT+hEAdIOVoNdf3aIQLdLam1oYFGu+o2qhAk/kTAnWAliMI1JiB+S5VLSJQ46eBQB2hCjSdPyFQB4gCXcldNjQCBTrnlf6FBGqKKKJAI6VtkwJN6E8I1AFijlYy1yWNMIHOO9FY/CKzEWi0g2qLAk3pTwjUAWqOCheo9mSfeaDJcrMx6YBAHSEJNKk/IVAHyAKNv0NXlSPN5597oN7GBRond5sTaKLhnyMQqB1yjuLv0lXlSPnx5z/QNy3QWMfU1gSa2p8QqAP0HEGg8nPzz5MTv0gINA3fOw/keDyGrgoypuzdKn/640oP9N9PLB3K3BlcZQ00dfWzRg3UhQg5ir1j15Uj8dMnmuYhfpEZ1UAjHVKZ1kC1rxgzuIA/IVAHYuQIAmWW0hznEKgDGxNo9EisQKB2ogg07r5dV464D5/sMv/mBer2i9Hmlzcl0CWAQO1E+jnvCIWMrCtHzGdP2Eu6cYG6/lyf+V0QKBEI1E6kH1OMUcjAunI0ncQpR5lAoA7VVAiUCARqZ6af86awshz1nz3tIL3tCzTCXP8QKBEI1M6afkxxKVwEmniQ89YF6jLRqjXfECgRCNROtF9iiFNMw8py1JzI6e8RiV9kbgK1zrRqLQMCJQKB2lnTb4EthVWgyf25fYHaZ1q1FwGBEoFA7azpp2yWwibQJe4RiV/kmgTqlnEIlAgEaifiPLixClpZjpbwZxECNU616lIEBEoEArWzpl9iWIoyjqPMBGo4nly/sSBQImUc+DRiTuMYqZwt5ygWJQvUucYPgRIp48CnsaaJxJeijOMoP4Fq56p2LAECJVLGgU9jTdM4LkUZx1FuAtUcTh5XnCFQImUc+DTizgERpZRt5ygOpQrUq8cOAiVSxoFPY02zkC1FGcdRhgKVDie/EQ8QKJEyDnwaC0yiY2PjOYpCCQKVjybPAWMQKJEyDnwase/Ai1DG1nMUgyIF6jvgFgIlUsaBTyP1HBAObD1HMShEoOJs/37rQ6BEyjjwaaS9hdmJzecoAkUIVJrt33N1CJRIGQc+jfjD98glbD9HdMoTaMANsxAokTIOfBoJ78BzZfs5olOKQCvFQ2cgUCJlHPg0Uo1/9qCAHJEpQ6DTsRQ0YQsESqSMA5/GHNf+ieuXkCMqxQi04v76AYESKePAp5Fi+J4nJeSISiECnX4vJWRlCJRIGQc+jdk7T/0pIkdEihJo6ISrECiRMg58GvO0vEhrl5EjGuUItAr2JwRKpYwDn8a81/6DKCNHNEoRKOkHUyBQImUc+DTmvHQVSCE5IlGUQEPXhUCJlHHg05jruCesW0qOKJQk0OB1IVAiZRz4NGY77sPXLSVHFIoRKAUIlMia9ulSzJWjOSoOS1HGcQSBpgECpZBdRMiRnTJyBIGmAQKlkF1EyJGdMnIEgaYBAqWQXUTIkZ0ycgSBpgECpZBdRMiRnTJyBIGmAQKlkF1EyJGdMnIEgaYBAqWQXUTIkZ0ycgSBpgECpZBdRMiRnTJyBIGmAQKlkF1EyJGdMnIEgaYBAqWQXUTIkZ0ycgSBpgECpZBdRMiRnTJyBIGmAQKlkF1EyJGdMnIEgaYBAqWQXUTIkZ0ycgSBpgECpZBdRMiRnTJyBIGmAQKlkF1EyJGdMnIEgaYBAqWQXUTIkZ0ycgSBpgECpZBdRMiRnTJyBIGmAQKlkF1EyJGdMnIEgaYBAqWQXUTIkZ0ycgSBpgECpZBdRMiRnTJyBIGmAQKlkF1EyJGdMnIEgaYBAqWQXUTIkZ0ycgSBpgECpZBdRMiRnTJyBIGmAQKlkF1EyJGdMnIEgaYBAqWQXUTIkZ0ycgSBpgECpZBdRMiRnTJyBIGmAQKlkF1EyJGdMnIEgaYBAqWQXUTIkZ0ycgSBpgECpZBdRMiRnTJyBIGmAQKlkF1EyJGdMnIEgaYBAqWQXUTIkZ0ycgSBpgECpZBdRMiRnTJyBIGmAQKlkF1EyJGdMnIEgaYBAqWQXUTIkZ0ycgSBpgECpZBdRMiRnTJyBIGmAQKlkF1EyJGdMnIEgaYBAqWQXUTIkZ0ycgSBpgECpZBdRMiRnTJyBIGmAQKlkF1EyJGdMnIEgaYBAqWQXUTIkZ0ycgSBpgECpZBdRMiRnTJyBIGmAQKlkF1EyJGdMnIEgaYBAqWQXUTIkZ0ycgSBpgECpZBdRMiRnTJyBIGmAQKlkF1EyJGdMnIEgaYBAqWQXUTIkZ0ycgSBpgECpZBdRMiRnTJyBIGmAQKlkF1EyJGdMnIEgaYBAqWQXUTIkZ0ycgSBpgECpZBdRMiRnTJyBIGmAQKlkF1EyJGdMnIEgaYBAqWQXUTIkZ0ycgSBpgECpZBdRMiRnTJyBIGmAQKlkF1EyJGdMnIEgaYBAqWQXUTIkZ0ycgSBpgECpZBdRMiRnTJyBIGmAQKlkF1EyJGdMnIEgfrx9IPr+/1bn2mfUrzebVxbYnYZXNM+XQrkyE4ZOYJAvXhyY9/w/d9pnlK83m9cW2R2GVzTPl0K5MhOGTmCQL24vX/ls/rx+/tXvlY/pXi937h/vEuxpn26FMiRnTJyBIH68Oh6W7d8cuOlXyifUrw+bNw/3qVY0z5dCuTIThk5gkB9uLt/tf/7Y+VTiteHjWvLzC6Da9qnS4Ec2SkjRxCoD7f3b7d/7/eiFJ9SvD5sXFtmdhlc0z5dCuTIThk5gkA9ePp+3zR/dH24yMk9pXj9ewPnAIDN8fuJpUOZGwgUABAXCDRMoMNAJe4pxesDaMJTyC4i5MhOGTlCE96DgBrouHFtodllcE37dCmQIztl5AgC9QACXYjsIkKO7JSRIwjUB/TCL0N2ESFHdsrIEQTqwzC+kxsHyjyleH3YuLbM7DK4pn26FMiRnTJyBIH6gDuRliG7iJAjO2XkCAL14en7+5eFe925pxSvDxv3j3cp1rRPlwI5slNGjiBQLx4zsy09ut7WM9mn+AVu4/7xLsWa9ulSIEd2ysgRBOrH4w9OfnyrrV/2AmWfEhbYjWtLzC6Da9qnS4Ec2SkjRxBoGiBQCtlFhBzZKSNHEGgaIFAK2UWEHNkpI0cQaBogUArZRYQc2SkjRxBoGiBQCtlFhBzZKSNHEGgaIFAK2UWEHNkpI0cQaBogUArZRYQc2SkjRxBoGiBQCtlFhBzZKSNHEGgaIFAK2UWEHNkpI0cQaBogUArZRYQc2SkjRxBoGiBQCtlFhBzZKSNHEGgaIFAK2UWEHNkpI0cQaBogUArZRYQc2SkjRxBoGiBQCtlFhBzZKSNHEGgaIFAK2UWEHNkpI0cQaBogUArZRYQc2SkjRxBoGiBQCtlFhBzZKSNHEGgaIFAK2UWEHNkpI0cQaBogUArZRYQc2SkjRxBoGiBQCtlFhBzZKSNHEGgaIFAK2UWEHNkpI0cQaBogUArZRYQc2SkjRxBoGiBQCtlFhBzZKSNHEGgaIFAK2UWEHNkpI0cQaBogUArZRYQc2SkjRxBoGiBQCtlFhBzZKSNHEGgaIFAK2UWEHNkpI0cQaBogUArZRYQc2SkjRxBoGiBQCtlFhBzZKSNHEGgavgcAAKsGAgUAgEAWFOiKEPMEZJAjO8iRA2tOEgSqZs37NBXIkR3kyIE1JwkCVbPmfZoK5MgOcuTAmpMEgapZ8z5NBXJkBzlyYM1JgkDVrHmfpgI5soMcObDmJEGgata8T1OBHNlBjhxYc5IgUDVr3qepQI7sIEcOrDlJECgAAAQCgQIAQCAQKAAABAKBAgBAIBAoAAAEAoECAEAgECgAAAQCgQIAQCAQKAAABAKBAgBAIBAoAAAEAoECAEAgECgAAAQCgRp5cuPV/tHjf9rvX/ofX08L+7c+6xaefnB9WigQQ47GhaJz9JcmE8qjRb9QHPok1ewhll2SIFAjt/f9nvvzvuXl3zULj663Cy/9oll4cqNd+P7vlotyWdQ54haKztGne93Rol8oDn2SGsZDLL8kQaAGnt7e93vupMxXPquffrp/5VSlevp+s/D4/XbhtHOZhfLQ5IhbKDpH9/cv/bRuPnx71t/WHjol58iUJPYQyzBJEKiev/xkP+y52/0uu71/u5FDu5uf3Gi+L7mF8tDlSJ+wwjh9277d/D3VnYyHTsk5MiWJO8QyTBIEquXufv+jP3d7btjBp2/KV/v/hifv9vv27v7HSwW6ILoc8QkrOUdPbvTtzdvNh+cyoV8oDUOS2EMsxyRBoFruvvy/J1f2X3mPrp8qVtz34G3WFMWhyxGfsLJz1NO6gcuEfqFY5CSxh1iOSYJAjSgFOl4DfVV8pUisAkWO6v7rFjkyIyepe155GuYBBGpk+upjr/M97foMf/R1nvs0McocsQvIUd23PiFQM3KSuuch0LUyyOHR9UaXjTib1vujn3RDdD7j9mlGYyuSoswRu4Actd3MvxCOFv3CYlEujCJJwwuiQLNJEgRqZLzYcrcbp/bfu2ugvRzU7Y3SUOaIXUCO6vvXX2qu3qEGakKVpP4V1EBXynS1uhlM8cPP+j6Rrg/wdtvdnN8+TYwyR+wCcnS3HyEOgRpQJql7CQJdK2J3333JmRl2DCZGlSN+ofQcfbofxi2iF16LJkncowyTBIEaEfdUU/nkBToMSctoaFpiVDniF8rO0dPb/Q2ttZAJ/UJ5aJPUMhxiGSYJAjUi7LlH17uhn1MTPsebIxKjzBG3UHaObjM3HuJOJA3aJLVM/ZTZJQkCNTLsue5e3b9cb7Vwfz91IjWDQl/O7PbcxGhyxCwUnaO77KfmMqFfKA59klqGQyzDJEGgRsbmaT9bTLfj+h7mrpL1OLsJYhKjzhG3UHCO+vmD9v393Fwm9AuFYUpSw3iI5ZckCNTIdH3vz/+43//tT/uF/9vMB/rDflbCxx80k4Nm85WYHE2OuIVyc3R/z7mBz4R+oSyMSarZQyy7JEGgAAAQCAQKAACBQKAAABAIBAoAAIFAoAAAEAgECgAAgUCgAAAQCAQKAACBQKBgTdzc7Z6dli7e2e2uLRcMABAoWBPfXmWVeYfTKQDJgUDBqri3233n3/rHD86mxwAsAQQK1sWpEf9i9wgNeLA4EChYF6dG/KWft4/QgAeLA4GClXFqxF/+U92atP0LwHJAoGBlNC33phF/c6iJnnj47tlud+mFj4blL5vl3ZU3/9SvcO2L13a77/5cURoAFCBQsDYenDXqvDdeC20b8x1/0y5e/HJYfqbpYzoJ9I1mAR1OIDoQKFgdzcXPkxXHBvxpual8fvPL3qCn5b85vfbwte4iaVNlPRn34c+WixhsFQgUrI7GiFemBvypRtp3Jt1pn/z2ar/cXyVFbz2YDQgUrI8HzRVOpgE/NM5Pqny26WXq3XpabF7p/wAQHwgUrJA7u6kB31mz4ybfMX9zECi668E8QKBghZyqoGMFtLm7c2Koa37z5W/ee243CBTjRcE8QKBghVgE+vlz7CIECmYDAgUrRBAo78em02i3u/LGP//xJgQK5gUCBSuEFah0ibMfxVQz10AhUDAPEChYIaxAmVuSWpdOQj3VTSFQMCsQKFghnEBPC70x7zUDPieB3sE1UDAzEChYIZxAG1E+87OTKH/VDW7qm/BfvrZr70CCQMF8QKBghfACne6F76Zpeq1feuFX7S1IECiYDQgUrBBBoPXDd5uBS3/V3+1+8evnmrmZPuw76CFQMBsQKAAABAKBAgBAIBAoAAAEAoECAEAgECgAAAQCgQIAQCAQKAAABAKBAgBAIBAoAAAEAoECAEAgECgAAAQCgQIAQCAQKAAABAKBAgBAIBAoAAAEAoECAEAgECgAAAQCgQIAQCAQKAAABAKBAgBAIP8f7nEpS7xcYzkAAAAASUVORK5CYII=" width="672" /></p>
<div class="sourceCode" id="cb421"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb421-1"><a href="#cb421-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_A9_portugal_counterfactual.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">5</span>, <span class="at">height =</span> <span class="dv">5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
<pre><code>## Warning: Removed 519 rows containing missing values (`geom_line()`).</code></pre>
<div class="sourceCode" id="cb423"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb423-1"><a href="#cb423-1" tabindex="-1"></a>robust6 <span class="ot">&lt;-</span>      <span class="fu">plot</span>(synth_robust_predictors, <span class="at">type =</span> <span class="st">&quot;gap&quot;</span>, <span class="at">id =</span> <span class="st">&quot;Portugal&quot;</span>, <span class="at">raw =</span> <span class="st">&quot;all&quot;</span>, <span class="at">legendOff =</span> <span class="cn">TRUE</span>, <span class="at">xlim =</span> <span class="fu">c</span>(<span class="sc">-</span><span class="dv">31</span>,<span class="dv">15</span>), <span class="at">ylim =</span> <span class="fu">c</span>(<span class="sc">-</span>.<span class="dv">5</span>,.<span class="dv">2</span>),  </span>
<span id="cb423-2"><a href="#cb423-2" tabindex="-1"></a>         <span class="at">main=</span><span class="st">&quot;&quot;</span>, <span class="at">ylab =</span> <span class="st">&quot;Gaps in Satisfaction: Synthetic minus Actual (Portugal)&quot;</span>, <span class="at">xlab =</span> <span class="st">&quot;Time relative to treatment (Years)&quot;</span>, </span>
<span id="cb423-3"><a href="#cb423-3" tabindex="-1"></a>         <span class="at">theme.bw =</span> <span class="cn">TRUE</span>)<span class="sc">+</span></span>
<span id="cb423-4"><a href="#cb423-4" tabindex="-1"></a>      <span class="fu">theme</span>( <span class="at">panel.grid.major =</span> <span class="fu">element_blank</span>(), <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb423-5"><a href="#cb423-5" tabindex="-1"></a>             <span class="at">panel.background =</span> <span class="fu">element_blank</span>(), <span class="at">axis.line =</span> <span class="fu">element_line</span>(<span class="at">colour =</span> <span class="st">&quot;black&quot;</span>))  </span></code></pre></div>
<p><img 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" width="672" /></p>
<div class="sourceCode" id="cb424"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb424-1"><a href="#cb424-1" tabindex="-1"></a> <span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_A9_portugal_gap.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">5</span>, <span class="at">height =</span> <span class="dv">5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
<pre><code>## Warning: Removed 10 rows containing missing values (`geom_line()`).</code></pre>
<div class="sourceCode" id="cb426"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb426-1"><a href="#cb426-1" tabindex="-1"></a>robust7 <span class="ot">&lt;-</span>     <span class="fu">plot</span>(synth_robust_predictors, <span class="at">type =</span> <span class="st">&quot;counterfactual&quot;</span>, <span class="at">id =</span> <span class="st">&quot;Spain&quot;</span>, <span class="at">raw =</span> <span class="st">&quot;all&quot;</span>, <span class="at">legendOff =</span> <span class="cn">TRUE</span>, <span class="at">shade.post =</span> <span class="cn">FALSE</span>,</span>
<span id="cb426-2"><a href="#cb426-2" tabindex="-1"></a>         <span class="at">main =</span> <span class="st">&quot;&quot;</span>, <span class="at">ylab =</span> <span class="st">&quot;Satisfaction with Democracy (Spain)&quot;</span>,  <span class="at">xlab =</span> <span class="st">&quot;Year&quot;</span>, <span class="at">ylim =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>),</span>
<span id="cb426-3"><a href="#cb426-3" tabindex="-1"></a>         <span class="at">theme.bw =</span> <span class="cn">TRUE</span>)<span class="sc">+</span></span>
<span id="cb426-4"><a href="#cb426-4" tabindex="-1"></a>      <span class="fu">theme</span>( <span class="at">panel.grid.major =</span> <span class="fu">element_blank</span>(), <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb426-5"><a href="#cb426-5" tabindex="-1"></a>             <span class="at">panel.background =</span> <span class="fu">element_blank</span>(), <span class="at">axis.line =</span> <span class="fu">element_line</span>(<span class="at">colour =</span> <span class="st">&quot;black&quot;</span>))  <span class="sc">+</span></span>
<span id="cb426-6"><a href="#cb426-6" tabindex="-1"></a>      <span class="fu">scale_color_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;black&quot;</span>, <span class="st">&quot;gray87&quot;</span>, <span class="st">&quot;gray40&quot;</span>)) <span class="sc">+</span> <span class="co">#first is the synth, then raw data, then actual</span></span>
<span id="cb426-7"><a href="#cb426-7" tabindex="-1"></a>      <span class="fu">scale_linetype_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;dashed&quot;</span>,<span class="st">&quot;solid&quot;</span>, <span class="st">&quot;solid&quot;</span>))</span></code></pre></div>
<pre><code>## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for linetype is already present.
## Adding another scale for linetype, which will replace the existing scale.</code></pre>
<p><img 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" width="672" /></p>
<div class="sourceCode" id="cb428"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb428-1"><a href="#cb428-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_A9_spain_counterfactual.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">5</span>, <span class="at">height =</span> <span class="dv">5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
<pre><code>## Warning: Removed 519 rows containing missing values (`geom_line()`).</code></pre>
<div class="sourceCode" id="cb430"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb430-1"><a href="#cb430-1" tabindex="-1"></a>robust8 <span class="ot">&lt;-</span>     <span class="fu">plot</span>(synth_robust_predictors, <span class="at">type =</span> <span class="st">&quot;gap&quot;</span>, <span class="at">id =</span> <span class="st">&quot;Spain&quot;</span>, <span class="at">raw =</span> <span class="st">&quot;all&quot;</span>, <span class="at">legendOff =</span> <span class="cn">TRUE</span>, <span class="at">xlim =</span> <span class="fu">c</span>(<span class="sc">-</span><span class="dv">31</span>,<span class="dv">15</span>), <span class="at">ylim =</span> <span class="fu">c</span>(<span class="sc">-</span>.<span class="dv">5</span>,.<span class="dv">2</span>), </span>
<span id="cb430-2"><a href="#cb430-2" tabindex="-1"></a>         <span class="at">main=</span><span class="st">&quot;&quot;</span>, <span class="at">ylab =</span> <span class="st">&quot;Gaps in Satisfaction: Synthetic minus Actual (Spain)&quot;</span>, <span class="at">xlab =</span> <span class="st">&quot;Time relative to treatment (Years)&quot;</span>, </span>
<span id="cb430-3"><a href="#cb430-3" tabindex="-1"></a>         <span class="at">theme.bw =</span> <span class="cn">TRUE</span>)<span class="sc">+</span></span>
<span id="cb430-4"><a href="#cb430-4" tabindex="-1"></a>      <span class="fu">theme</span>( <span class="at">panel.grid.major =</span> <span class="fu">element_blank</span>(), <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb430-5"><a href="#cb430-5" tabindex="-1"></a>             <span class="at">panel.background =</span> <span class="fu">element_blank</span>(), <span class="at">axis.line =</span> <span class="fu">element_line</span>(<span class="at">colour =</span> <span class="st">&quot;black&quot;</span>))  </span></code></pre></div>
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" width="672" /></p>
<div class="sourceCode" id="cb431"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb431-1"><a href="#cb431-1" tabindex="-1"></a> <span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_A9_spain_gap.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">5</span>, <span class="at">height =</span> <span class="dv">5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
<pre><code>## Warning: Removed 10 rows containing missing values (`geom_line()`).</code></pre>
<div class="sourceCode" id="cb433"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb433-1"><a href="#cb433-1" tabindex="-1"></a>robust9 <span class="ot">&lt;-</span>     <span class="fu">plot</span>(synth_robust_predictors, <span class="at">type =</span> <span class="st">&quot;counterfactual&quot;</span>, <span class="at">id =</span> <span class="st">&quot;Mexico&quot;</span>, <span class="at">raw =</span> <span class="st">&quot;all&quot;</span>, <span class="at">legendOff =</span> <span class="cn">TRUE</span>, <span class="at">shade.post =</span> <span class="cn">FALSE</span>,</span>
<span id="cb433-2"><a href="#cb433-2" tabindex="-1"></a>         <span class="at">main =</span> <span class="st">&quot;&quot;</span>, <span class="at">ylab =</span> <span class="st">&quot;Satisfaction with Democracy (Mexico)&quot;</span>,  <span class="at">xlab =</span> <span class="st">&quot;Year&quot;</span>, <span class="at">ylim =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>),</span>
<span id="cb433-3"><a href="#cb433-3" tabindex="-1"></a>         <span class="at">theme.bw =</span> <span class="cn">TRUE</span>)<span class="sc">+</span></span>
<span id="cb433-4"><a href="#cb433-4" tabindex="-1"></a>      <span class="fu">theme</span>( <span class="at">panel.grid.major =</span> <span class="fu">element_blank</span>(), <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb433-5"><a href="#cb433-5" tabindex="-1"></a>             <span class="at">panel.background =</span> <span class="fu">element_blank</span>(), <span class="at">axis.line =</span> <span class="fu">element_line</span>(<span class="at">colour =</span> <span class="st">&quot;black&quot;</span>))  <span class="sc">+</span></span>
<span id="cb433-6"><a href="#cb433-6" tabindex="-1"></a>      <span class="fu">scale_color_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="st">&quot;black&quot;</span>, <span class="st">&quot;gray87&quot;</span>, <span class="st">&quot;gray40&quot;</span>)) <span class="sc">+</span> <span class="co">#first is the synth, then raw data, then actual</span></span>
<span id="cb433-7"><a href="#cb433-7" tabindex="-1"></a>      <span class="fu">scale_linetype_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;dashed&quot;</span>,<span class="st">&quot;solid&quot;</span>, <span class="st">&quot;solid&quot;</span>))</span></code></pre></div>
<pre><code>## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for linetype is already present.
## Adding another scale for linetype, which will replace the existing scale.</code></pre>
<p><img 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" width="672" /></p>
<div class="sourceCode" id="cb435"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb435-1"><a href="#cb435-1" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_A9_mexico_counterfactual.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">5</span>, <span class="at">height =</span> <span class="dv">5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
<pre><code>## Warning: Removed 541 rows containing missing values (`geom_line()`).</code></pre>
<div class="sourceCode" id="cb437"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb437-1"><a href="#cb437-1" tabindex="-1"></a>robust10 <span class="ot">&lt;-</span>     <span class="fu">plot</span>(synth_robust_predictors, <span class="at">type =</span> <span class="st">&quot;gap&quot;</span>, <span class="at">id =</span> <span class="st">&quot;Mexico&quot;</span>, <span class="at">raw =</span> <span class="st">&quot;all&quot;</span>, <span class="at">legendOff =</span> <span class="cn">TRUE</span>, <span class="at">xlim =</span> <span class="fu">c</span>(<span class="sc">-</span><span class="dv">31</span>,<span class="dv">15</span>), <span class="at">ylim =</span> <span class="fu">c</span>(<span class="sc">-</span>.<span class="dv">5</span>,.<span class="dv">2</span>), </span>
<span id="cb437-2"><a href="#cb437-2" tabindex="-1"></a>         <span class="at">main=</span><span class="st">&quot;&quot;</span>, <span class="at">ylab =</span> <span class="st">&quot;Gaps in Satisfaction: Synthetic minus Actual (Mexico)&quot;</span>, <span class="at">xlab =</span> <span class="st">&quot;Time relative to treatment (Years)&quot;</span>, </span>
<span id="cb437-3"><a href="#cb437-3" tabindex="-1"></a>         <span class="at">theme.bw =</span> <span class="cn">TRUE</span>)<span class="sc">+</span></span>
<span id="cb437-4"><a href="#cb437-4" tabindex="-1"></a>      <span class="fu">theme</span>( <span class="at">panel.grid.major =</span> <span class="fu">element_blank</span>(), <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb437-5"><a href="#cb437-5" tabindex="-1"></a>             <span class="at">panel.background =</span> <span class="fu">element_blank</span>(), <span class="at">axis.line =</span> <span class="fu">element_line</span>(<span class="at">colour =</span> <span class="st">&quot;black&quot;</span>))  </span></code></pre></div>
<p><img src="data:image/png;base64,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" width="672" /></p>
<div class="sourceCode" id="cb438"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb438-1"><a href="#cb438-1" tabindex="-1"></a> <span class="fu">ggsave</span>(<span class="st">&quot;C:/Users/snpwi/Dropbox/Quota-Satisfaction/EJPR Submission/Data Analysis/Figures/figure_A9_mexico_gap.pdf&quot;</span>, <span class="at">width =</span> <span class="dv">5</span>, <span class="at">height =</span> <span class="dv">5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>)</span></code></pre></div>
<pre><code>## Warning: Removed 21 rows containing missing values (`geom_line()`).</code></pre>
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